Efficient processing and sharing of geological environmental data are crucial for sustainable development and informed decision-making. However, current analysis methods struggle with low efficiency and resource utilization, especially in complex computational tasks. This paper proposes a parallel processing framework based on container orchestration that systematically improves the efficiency of geological environment data analysis by integrating container technology and complex task processing optimization strategies. Leveraging containerized processing, we established a standardized packaging and deployment mechanism for geological environmental data analysis algorithms, enabling flexible encapsulating and management of multiple models. In addition, we proposed a complex task decomposition method for pipeline parallelism and realized multicontainer collaborative geological environment data processing in a distributed environment based on container orchestration. For enhancing the efficiency of complex task processing purposes, this paper proposed a task scheduling optimization strategy based on the dynamic merging of directed acyclic graph, which improves resource utilization and processing speed through task merging. Experimental results demonstrate that the proposed framework enhances processing efficiency by over 50% in typical geological environmental data analysis scenarios, while improving resource utilization by$\mathbf{4 8 \% - 6 9 \%}$. It exhibits strong reliability and scalability, offering technical support for intelligent analysis and service sharing of geological environmental data.

Three-dimensional (3D) reconstruction based on aerial images of unmanned aerial vehicles (UAVs) is important for surveying and monitoring natural resources, supporting land cover analysis and geological hazard mapping. However, the performance of a fixed UAV docking station and manual deployments in large areas is limited by slow data acquisition and transmission. To accelerate data acquisition, emerging mobile UAV docking stations can collaboratively capture images in wide areas. Moreover, by leveraging edge computing power on UAVs and docking stations, the computations of 3D reconstruction can be performed locally, eliminating the need for data transmission. This paper builds a mathematical model for the entire process. The model can be decomposed into three associated problems: area partitioning, computation offloading, and mobile docking station path planning. The optimization objective is to minimize the execution time under the constraint of UAV battery power. A suboptimal solution is first derived using an enumeration-genetic algorithm and then fine-tuned using deep reinforcement learning. The experimental results validate the feasibility of using mobile docking stations for 3D reconstruction. In addition, the numerical results indicate that our proposed solution reduces execution time compared to the benchmark solution.

Remote sensing image target detection plays a pivotal role in earth observation, offering substantial value for applications such as urban planning and environmental monitoring. Due to the significant scale variations among targets, complex backgrounds with dense small object distributions, and strong inter-target scene correlations, existing target detection methods usually fails to effectively model target relationships and contextual information for remote sensing imagery. To address these limitations, we proposed YOLO-ALS, a novel remote sensing target detection network that integrates adaptive local scene context. The proposed framework introduces three key points: First, a full-dimensional dynamic convolution reconstruction C2f module enhances target feature representation by overcoming local context extraction limitations and target co-occurrence prior deficiencies. Second, an adaptive local scene context module dynamically integrates multi-scale receptive field features through spatial attention, enabling background window adaptive selection and cross-scale feature alignment. Finally, a co-occurrence matrix-integrated classification auxiliary module mines target association rules through data-driven learning, correcting classification probabilities in low-confidence areas by combining high-confidence areas co-occurrence information with optimal threshold, which can significantly reduce missed detection rates. Comprehensive experiments on multiple public remote sensing datasets demonstrate the superiority of the proposed method through extensive ablation studies and comparative analyses. The proposed method has achieved state-of-the-art performance while addressing the unique challenges of remote sensing target detection.

Urban Informal Settlements (UIS) denote densely populated locales characterized by inadequate urban infrastructure standards, often exhibiting an amalgamation of rural and urban attributes, primarily situated within the confines of major cities or metropolitan regions. UIS mapping is a typical task that aims to identify pixels corresponding to urban informal settlements in remote sensing images. The extremely similar visual characteristics, sample uncertainty, and highly manual costs bring large-scale UIS mapping noteworthy challenges. In this paper, we propose a sample uncertainty-aware semi-supervised learning method guided by background-knowledge from crowdsourced geospatial data (namely SemiUIS) for UIS mapping and produces a 1-meter resolution UIS map in the Urban Agglomeration in the Middle Reaches of the Yangtze River, China (UAMRYR). Our proposed SemiUIS method integrates semisupervised learning with the guidance of background knowledge from crowdsourcing data to reduce the uncertainty of the generated pseudo-semantic segmentation samples to improve the quality of the constructed data set by jointly optimizing the background (Not UIS areas) and the foreground (UIS areas). In contrast, traditional semi-supervised learning methods only focus on explicitly optimizing the foreground (UIS areas) in the pseudo-sample generation process. Experiments were conducted in the 31 prefecture-level cities of UAMRYR. Visual interpretation of labeled remote sensing samples, street view images and two novel knowledge validation indicators, Global Knowledge Precision (GKP) and Local Knowledge Precision (LKP) are proposed to verify the UIS mapping results. The proposed method reached an overall accuracy (OA) of 90.58 %, mean intersection over union (mIoU) of 75.88 %, Accuracy (Acc) of 78.77 %, Intersection over Union (IoU) of 62.97 %, GKP of 90.33 %, and LKP of 85.01 %, respectively with only 36 (5%) training samples. This work will be available at https://github.com/RunyuFan/UisYangtze.

Off-road trafficability (ORT) in complex geological environments is crucial for special operations, emergency rescue, and natural resource development. ORT is influenced by a combination of geographical and geological environmental factors. Recent studies primarily employ rule methods for ORT assessment. These approaches are labour-intensive, lack timeliness and objectivity, and are constrained to limited data sources. To address these issues, this work proposes a new method for ORT assessment that combines remote sensing imagery with geographic and geological data (RSI-factors) through a cross-modal rectified fusion network (CRFNet). This network is designed with the feature rectification module (FRM) and feature fusion mixer module (FFMM) to integrate cross-modal features. To address potential missing data in complex geological environments, this work proposes a multi-task and prompt learning strategy to improve model robustness. Experiments conducted on our Asia Dataset and Africa Dataset yielded optimal evaluation results. On the complete Asia and Africa Dataset, the CRFNet model improved overall accuracy (OA) by more than 25% and the kappa coefficient by over 43% compared to rule methods. On the incomplete Asia and Africa Dataset, the CRFNet model improved OA by more than 5.5% and the kappa coefficient by over 8% compared to suboptimal deep learning (DL) models. To the best of our knowledge, this research work is the first in which DL features have been combined with multi-modal RSI-factors data for ORT assessment, paving a new path for research in this field. The source codes of this work will be made publicly available at https://github.com/kangkanghe/CRFNet.

The ground object distribution in remote sensing images exhibits strong regularities. However, existing deep learning-based object detection models often focus solely on instance-level information within sample labels, neglecting the modeling of relationships between instances. Additionally, these models fail to effectively utilize the substantial background information present in remote sensing images. To address these issues, we propose a remote sensing object detection network named SCENE-YOLO, building upon the YOLOv8 architecture and introducing scene supervision. First, we introduce a scene information gathering and distribute network (SGD), based on transformer, to inject high-level semantic information into the feature pyramid. A slice-and-distribute mechanism is employed to prevent information loss during feature fusion across layers. Second, the backbone network is redesigned, incorporating the attention mechanism of omni-dimensional dynamic convolution (ODConv) to dynamically redistribute weights for target features. Subsequently, a scene label generation algorithm (SLGA) based on prototype learning is proposed to supervise the model by generating scene-level labels, modeling instance-instance relationships through the introduction of artificial knowledge and multilevel classification. Finally, a scene-assisted detection head (SADHead) is introduced to enhance detection performance in complex backgrounds by leveraging scene features with global contextual information to assist the model in target classification. Experimental validation on the publicly available DOTA and DIOR datasets demonstrates the effectiveness and superiority of the proposed algorithm.

In Earth observation, cloud severely affects the interpretation of optical satellites generated high-resolution images. Cloud-free optical images are vital for downstream tasks such as semantic segmentation and object detection. Thus, the elimination of clouds from optical imagery has emerged as a significant topic in remote sensing. Currently, most existing methods are proposed to leverage the texture information from auxiliary synthetic aperture radar (SAR) images to restore cloud-free images via direct channel merging. However, such a unified feature extraction approach often neglects the inherent distribution disparity between SAR and optical images—the result of differing imaging principles-potentially leading to significant feature loss. To this end, we introduce a network by jointing SAR and optical images multimodal and cross-attention network (MMCANet) to effectively extract multiscale contextual features from SAR imagery and integrate them with optical features. Specifically, instead of simple concatenation of the channels of SAR and optical images, we obtain high-dimensional features from them through independent feature extractors. The integration of these features is facilitated by a cross-attention mechanism that provides a more fine-grained amalgamation of information. Meanwhile, an atrous spatial pyramid pooling (ASPP) module is introduced into the integration of high-level features, which captures multiscale contextual information around clouded areas. In addition, we propose four advanced remote sensing image restoration algorithms that approach image restoration as a series of subtasks, gradually eliminating clouds to enhance performance. Comprehensive assessments show that MMCANet performs well on the SEN 12 MS-CR dataset with peak signal-to-noise ratio (PSNR) of 39.8871, structural similarity index (SSIM) of 0.9672, mean absolute error (MAE) of 0.0081, and spectral angle mapper (SAM) of 2.9884.

Spatial-regularized spectral unmixing has achieved great progress and attracted widespread attention for addressing homogeneous regions with identical spectral characteristics. In this paper, a new hyperspectral unmixing algorithm with graph Laplacian regularization and local collaborative sparse regression is proposed, based on superpixel segmentation, namely GLCGSU. Considering mixed pixels in homogeneous areas have similar endmembers and abundances, we utilize superpixel image clustering (SLIC) to cluster similar pixels, leveraging boundary information for uniform area extraction. Spatial similarity is investigated via graph Laplacian regularization. Meanwhile, we apply local collaborative weighted sparse regression to achieve abundance matrix sparsity. Experimental results demonstrates the effectiveness of the proposed method both on simulated and real data, proving its superiority for hyperspectral unmixing.

The integration of remote sensing and citizen science offers an unprecedented opportunity for observing the Earth and human activities. Recently, heterogeneous data fusion models have been proposed to align representations and geolocations of remote sensing imagery and social media data, such as optimal transport (OT) and geographic OT (GOT). However, these models generally ignore the differences in remote sensing features of the same geographical phenomenon for different land cover types, which not only affects the fusion accuracy but also leads to the loss of fusion information. In this study, we develop a general model for heterogeneous SAR imagery and social media data fusion based on OT and land cover information, namely, land cover information-constraint OT (LCIOT). Taking the 2017 Houston flood event as a case study, the experimental findings demonstrate that the proposed LCIOT can align 99% of geotagged Twitter data to flooded areas with an average transport distance of 710 m, outperforming the state-of-the-art models, i.e., OT and GOT. By combining the supervised information obtained by LCIOT with the SAR imagery, the overall accuracy (OA) and Kappa of urban flood mapping in the three study areas ranges from 0.77 to 0.82 and 0.51 to 0.61, respectively. Overall, the proposed LCIOT provides a new perspective to solve the issues of discrepancies in data distribution and geolocation uncertainty in the context of heterogeneous data fusion.

Supervised hyperspectral image classification suffers from the overfitting problem when limited labels are available. Graph-based semisupervised classifiers can tackle this problem by building connections between labeled and unlabeled samples. In this work, we prove the following two propositions for an optimal graph: 1) the interclass connection weights must be 0 and 2) for a given class, a subset must contain labeled samples or be connected to the remaining subset. In a semisupervised scenario, it is very difficult to ensure that the aforementioned propositions hold. Here, we introduce a new discriminant superpixel graph (dSPG) to build a suboptimal graph, which combines a newly proposed within-superpixel graph, aimed at disconnecting pixels belonging to different classes in a superpixel (so as to decrease interclass connection weights) and a between-superpixel graph that connects spectral adjacent superpixels (to increase the intraclass subset connections). We further propose a dSPG regularizer for hyperspectral image classification and a dSPG-guided graph convolutional network (dSPGCN) to extract discriminant features. Experimental results on real hyperspectral datasets demonstrate the good performance of our newly proposed dSPG for semisupervised hyperspectral image classification. The source codes for this study are available athttps://github.com/yulong112/dSPG.

Deep learning-based building instance extraction on remote sensing imagery (RSI) has achieved tremendous success under the large-scale labeled training data. However, multi-target domain adaptation building instance extraction (MD-BIE) is still a challenge task that involves transferring knowledge from a source domain to multiple unlabeled target domains, which poses various semantic gaps between and within multiple domains,e.g., style, illumination, resolution, density, scale, etc. Most current methods for single-target domain adaptation are not applicable to the more realistic MD-BIE task. To this end, we propose a novel Domain-common Approximation Learning (DAL) for both modelling intra-domain and inter-domain adaptation, thus obtaining robust MD-BIE. DAL contains three main modules: multi-domain style transfer (MST), multi-domain feature approximation (MFA), and multi-domain cascaded instance extraction (MCIE). To alleviate the semantic gaps between multiple domains for inter-domain adaptation, we first employ the MST to learn multiple target-domain-like features that preserve both the styles of target domains and the content of the source domain, and then use the MFA to approximate these features towards a central domain-common space, thus producing domain-common semantic representations. Moreover, we develop the MCIE with hierarchical extraction losses for intra-domain adaptation to extract precise building instance contours from the domain-common semantic representations, further eliminating the potential gaps within multiple domains. By co-learning these three modules in an end-to-end manner, the DAL bridges the semantic gaps between and within multiple domains. Extensive experiments on different popular MD-BIS tasks (SAB → Crowd & WHU, Crowd → SAB & WHU, SAB → Crowd & SAB & WHU and SAB → WHU) show that our DAL outperforms the current methods by a significant margin.

Hyperspectral band selection aims to identify an optimal subset of bands for hyperspectral images (HSIs). For most existing clustering-based band selection methods, they directly stretch each band into a single feature vector and employ the pixelwise features to address band redundancy. In this way, they do not take full consideration of the spatial information and deal with the importance of different regions in HSIs, which leads to a nonoptimal selection. To address these issues, a region-aware hierarchical latent feature representation learning-guided clustering (HLFC) method is proposed. Specifically, in order to fully preserve the spatial information of HSIs, the superpixel segmentation algorithm is adopted to segment HSIs into multiple regions first. For each segmented region, the similarity graph is constructed to reflect the bands-wise similarity, and its corresponding Laplacian matrix is generated for learning low-dimensional latent features in a hierarchical way. All latent features are then fused to form a unified feature representation of HSIs. Finally, k -means clustering is utilized on the unified feature representation matrix to generate multiple clusters from which the band with maximum information entropy is selected to form the final subset of bands. Extensive experimental results demonstrate that the proposed clustering method can achieve superior performance than the state-of-the-art representative methods on the band selection. The demo code of this work is publicly available at https://github.com/WangJun2023/HLFC.

A variable length sequential iterable convolutional recurrent network (VS-ICRN) is proposed in this paper, aiming at improving the vehicle target recognition ability for the Ultra-Wideband Impulse Radar (UWB-IR). Firstly, the array imaging technology is introduced into the UWB-IR, and thus a range-angle imaging method for the array UWB-IR is put forward, to simulate the array UWB-IR vehicles image under different observation conditions. Secondly, in order to make full use of both the deep features in the single image and the deep associated features between the sequence images, a VS-ICRN model is proposed, which includes three sub-modules: the image feature extraction based on the iterable convolution, the variable length sequential image associated feature extraction and the target classification, respectively. Finally, the experiment on the simulation dataset and the MSATAR is carried out to validate the effectiveness of the proposed method. The experimental results on the simulation dataset show that when SNR=-10dB, the proposed method is superior in the recognition rate to the GoogLeNet and AlexNet methods with 16% and 19%, respectively. Meanwhile, the proposed VS-ICRN method only needs 1.38% parameters quantity to achieve a comparable recognition rate as GoogLeNet on MSTAR dataset.

Remote sensing change detection (RSCD) aims to explore surface changes from co-registered pair of images. However, the high cost of memory and computation in previous convolutional neural network (CNN)-based methods prevent their successes from being applied to real-world applications. Therefore, we propose a novel lightweight network, which identifies changes based on the features extracted by mobile networks via progressive feature aggregation and supervised attention, termed as A2Net. Considering the less powerful representation capability of mobile networks, we design a neighbor aggregation module (NAM) to fuse features within nearby stages of the backbone to strengthen the representation capability of temporal features. Then, we propose a progressive change identifying module (PCIM) to extract temporal difference information from bitemporal features. Besides, we design a supervised attention module (SAM) to reweight features for effectively aggregating multilevel features from high levels to low levels. With NAM, PCIM, and SAM incorporated, A2Net can achieve favorable results compared with the state-of-the-art methods on three challenging RSCD datasets with fewer parameters (3.78 M) and lower computation costs (6.02 G). The demo code of this work is publicly available athttps://github.com/guanyuezhen/A2Net.

Soil elements refer to different types of soil with unique colors, textures, and particle sizes. Their interpretation is essential for agriculture, ecological environment and land permeability assessment. This typically requires experts with dual knowledge in geology and remote sensing. With the increasing volume of remote sensing data, the traditional “visual interpretation" and “field survey" technique is no longer sufficient to meet the demands. Because of the challenges such as the fine structure of soil, complex and variable natural scenes, and strong spatial variability, there remains a considerable gap between the accuracy of deep learning-based methods and expert interpretation. To improve the accuracy of intelligent soil elements interpretation, this study proposes a soil interpretation framework coupling implicit knowledge with multispectral image (SIFCIM). This framework quantifies implicit knowledge, such as interpretation symbol and terrain feature, into matrix data (interpretation symbol distance field and digital elevation model). To align with the SIFCIM, an Implicit-Knowledge-Guided Adaptive Feature Fusion Network (IAFFNet) is constructed, which enhances the utilization efficiency of auxiliary features through an adaptive implicit feature fusion module and a global feature dependence module. Experimental results demonstrate that IAFFNet outperforms interpretation methods with single remote sensing image, achieving approximately 4.34% and 6.62% improvements in overall pixel accuracy and mean intersection over union, respectively. These results validate the effectiveness and robustness of the implicit-knowledge-guided approach in soil elements interpretation. To our knowledge, this work is the first to apply the concept of implicit knowledge to soil elements interpretation, providing a novel insight for related research.

Due to the complex and highly heterogeneous land cover in urban areas, the single-temporal pixel-wise and parcel-wise classification cannot realize high-precision recognition of ground objects. Semantic segmentation of satellite image time series (SITS), can distinguish objects with similar spectral reflection and temporal evolution. But optical SITS have problems of uneven time-frequency distribution and incomplete, which makes it impossible to directly use existing models to carry out time series semantic segmentation. This study proposes a semantic segmentation network that combines optical and radar SITS, named Multi-Source Temporal Attention Fusion-Based Temporal-Spatial Transformer (MTAF-TST), to achieve high-precision land cover classification in urban areas. Firstly, MTAF-TST uses the Transformer spatial semantic segmentation module to extract the spatial context information of ground objects to realize pixel-level land cover classification, which relieves the salt-and-pepper phenomenon that is easy to occur in traditional pixel-by-pixel classification in complex scenes. Secondly, MTAF-TST uses the Transformer time feature extraction module to mine long-range time-dependent and high-level semantic information, overcoming the drawbacks of traditional convolutional and recurrent neural networks that cannot mine long-range time-dependent features of SITS. Finally, MTAF-TST uses a multi-source temporal attention fusion module to fuse the depth features of optical and radar SITS, which overcomes the shortcomings of traditional direct feature stitching methods that cannot make full use of time-correlated features, achieving high-precision land cover classification. The experimental results show that the MTAF-TST can realize the complementarity of radar and optical SITS in terms of timing integrity, color, texture, etc., and effectively improve the accuracy of SITS classification.

Lithological unit classification (LUC) refers to the classification of different types of rocks within an area, and it has been widely used in many fields, such as resource surveys and infrastructure planning. However, traditional field surveys require a lot of resources and time. Since remote sensing technology can rapidly acquire information without regional limitations, many researchers have focused on classifying lithological units with remote sensing images. However, in an area covered by vegetation, the beneficial information directly provided by remote sensing images is limited. Moreover, lithological interpretation often requires geological prior knowledge for guidance, which cannot be provided by remote sensing images. Thus, this study designed a dual-branch deep learning model to extract geological prior knowledge from geological information, and improve the accuracy of LUC. In the process of feature transmission of the model, a Dense Attention residual - Atrous Spatial Pyramid Pooling (DA-ASPP) module was proposed to maximize the preservation of lithological units’ features. The DA-ASPP integrates the idea of dense connection into ASPP for multiscale object feature preservation and adds residual structure into the channel attention mechanism to screen out the representative features of lithological units. The study area was located in southeastern Hubei Province, China, with seven categories of lithological units. A total of seven deep-learning networks were compared. The proposed method achieved a mean Intersection over Union (IOU) of 44.61% with a Macro-F1 of 56.54%, which were better than those of comparison models. Moreover, the visualization results demonstrated the superiority of the proposed model in LUC.

Land cover classification in mining areas (LCMA) is essential for the environmental assessment of mines and plays a crucial role in their sustainable development. The shapes of mine land occupation elements are irregular, and the overall proportion of their area is relatively small. Therefore, their features may be easily lost during feature extraction, which limits the interpretation accuracy in mining areas. This study attempts to address these issues. We propose a model named EG-UNet to enhance the features of elements with few samples and to capture long-range information. The proposed EG-UNet includes two main modules. First, the edge feature enhancement module, the edges of elements of mine land occupation contain more information than other spatial locations. Hence, during the feature extraction of elements, a Sobel operator is used to extract the object boundary, which increases the weight of these features before the pooling operation for their preservation. Second, the long-range information extraction module, long-range information helps extract tiny objects, such as dumping grounds in the mining area. We present a graph convolutional network (GCN) to capture the long-range features and apply convolutional neural networks to learn the graph construction. A total of ten deep-learning networks were compared using the LCMA semantic segmentation dataset. Our model exhibited the best performance, especially in classifying classes with few samples. Furthermore, to evaluate the general ability of EG-UNet, a benchmark-Gaofen Image Dataset (GID) was used, and the result still reflected the superiority of our method.

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

The active population is an indicator of urban vitality, representing socioeconomic vitality. An accurate mapping for the active population is the foundation for supporting regional sustainable development. Current research mainly relies on human mobile location data, which are challenging to access due to privacy and data sharing concerns. Thus, it is essential to map a wide area of the active population cost-effectively. This study demonstrates how an end-to-end deep learning approach can be used to reliably estimate urban active population distribution from remote sensing imagery. Learning based on the object and regional feature may provide insights into active population distribution by coupling ResNet-50 and Fully Convolutional Networks to achieve economic features that fuse Google surface features and night-light remote sensing data. Our results demonstrate the feasibility of using multiple remote sensing data to quantify the distribution of active populations. The proposed model$(\text{ResNet}-50+\text{FCN},\ R^{2}=0.82)$can better explain the differences in active population estimation than the ResNet-50 model ($R^{2}=0.61$). The study provides new insights into estimating active populations in cities that lack location-based service records.

Recently, deep-learning-based methods, especially deep convolutional neural networks (DCNNs), have effectively shown state-of-the-art performance in road extraction from high resolution remote sensing images (HRSI). However, due to the loss of location information and global context information, most existing DCNNs are inadequate for extracting tiny roads or roads which are severely occluded, leading to incomplete and discontinuous results. To address this problem, this paper proposes a graph-based dual convolutional network (GDCNet), which combines graph convolutional network (GCN) and convolutional neural network (CNN). In this model, GCN and CNN branches perform feature learning on large-scale irregular regions and small-scale regular regions, and generate complementary spatial-spectral features at superpixel and pixel levels, respectively. Then, a graph decoder is utilized to propagate features between graph nodes and image pixels, enabling the GCN and CNN to collaborate in a single network. Extensive experiments on two benchmark datasets demonstrate that the proposed GDCNet is competitive compared with other state-of-the-art methods both qualitatively and quantitatively, and is effective against the incomplete and discontinuous problems of the extracted roads.

Small water bodies and their temporal changes are, especially in urban areas, closely related to the urban climate, people's daily life, among others. Mapping of small water bodies with optical remote sensing images in complex urban landscapes is challenging: that is to establish a balance between reducing incorrect water detection and increasing the integrity of water extraction. In this work we propose a spatial information-integrated small water bodies mapping (SWM) method to achieve a complete and accurate extraction and temporal change monitoring of small water bodies. The spatial contextual information is exploited by the proposed water index roughness feature to compensate for the indistinguishability of small water bodies in spectral information. Results using Landsat and Sentinel-2 data show that the proposed algorithm achieves better water extraction performance, i.e. higher completeness and less incorrect extractions. It proves the ability to observe the changes of surface water.

Sparse unmixing aims at finding the optimal subset of endmembers in a spectral library to approximate the observed data, and has received increasing attention as it can circumvent the estimation of the endmember. In this paper, a graph Laplacian regularized spectral-spatial-sparse unmixing algorithm is proposed, namely, gLapS3U, incorporating the graph Laplacian regularization to consider the similarity between pixels of the whole image, and enforcing the spectral-spatial-sparse constraints to enhance the local spatial information as well as the sparsity of the abundance solution jointly. Experimental results on simulated and real data show the superiority of the proposed algorithm compared with state-of-the-art existing methods.

Visualizing the associated data of urban geological disasters can better describe the urban spatial information and provide technical support for analyzing geological disasters and upper-level decision-making. Aiming at the multi-source data with various types and complex structures in the urban space, how to integrate, organize, and visualize them is a critical technical problem for the urban geological disaster big data system. This paper deeply researched and discussed the visualization technology and implementation methods of multiple data types related to urban geological disasters. At the same time, we developed a visualization analysis system for urban geological disasters, which realized the integrated visualization of multi-source data in urban space and simulation visualization of geological disasters process. In addition, it can also provide users with related analysis functions of urban geological disasters based on the visualization system.

Due to the limitations of the imaging sensor, the spatial resolution of satellite imagery is often insufficient, namely, low resolution (LR). Therefore, super-resolution (SR) is proposed, which strives to improve image resolution, perfectly to compensate for the shortcomings of satellite sensor imaging. In this study, we develop a unique dilated convolution network with gradient prior (DCNG) for remote sensing SR, aiming to extract powerful low-level features with gradient prior and efficitive network and then reconstruct the high-level feature details. The DCNG is built of two components: the Multi-Scale Feature Extraction Network and the Feature Reconstruction Network. In the Multi-Scale Feature Extraction Network, the Double-Path Dilated Residual Block (DPDRB) is designed with the dilation convolution operation to obtain the multi-scale features and increase the receptive field, the Global Self-attention Module (GSA) to catch the long-range dependency among picture patches, and a Gradient Propagation Network (GPN) is proposed to extract high-level gradient information. In the Feature Reconstruction Network, the Pixel Shuffle is introduced to reconstruct the feature by combining characteristics of different frequency bands. Experiments using Massachusetts_Roads and 3K VEHICLE_SR data sets indicate that our DCNG surpasses state-of-the-art algorithms in terms of quantitative and qualitative evaluations.

As a hot topic in computer vision, target tracking has a vital application in many scientific and technological fields. The tracking method based on correlation filtering transforms the target tracking from the time domain to the frequency domain through the Fourier transform, which can boost the accuracy and success rate. However, in the complex tracking environments, the target tracking process may be affected by deformation, occlusion, and other inferences, which make the traditional target tracking algorithms hardly accommodate the requirements of robustness. Aiming the target tracking in complex scenes, this paper tries to improve the feature extraction based on the Convolution Neural Network, which can learn deep features of the target from different convolution layers with more abstract characteristics. Then these multiple features are fused to enhance the robustness performance of the traditional Kernel Correlation Filter algorithm from the aspects of model characteristics. Furthermore, the accuracy and success rate of the proposed algorithm are verified based on comprehensive comparative experiments in the Object Tracking Benchmark with variant interferences.

Change detection of high-resolution remote sensing images is an important task in earth observation and was extensively investigated. Recently, deep learning has shown to be very successful in plenty of remote sensing tasks. The current deep learning-based change detection method is mainly based on conventional long short-term memory (Conv-LSTM), which does not have spatial characteristics. Since change detection is a process with both spatiality and temporality, it is necessary to propose an end-to-end spatiotemporal network. To achieve this, Conv-LSTM, an extension of the Conv-LSTM structure, is introduced. Since it shares similar spatial characteristics with the convolutional layer, L-UNet, which substitutes partial convolution layers of UNet-to-Conv-LSTM and Atrous L-UNet (AL-UNet), which further using Atrous structure to multiscale spatial information is proposed. Experiments on two data sets are conducted and the proposed methods show the advantages both in quantity and quality when compared with some other methods.

Albeit great success has been achieved in image defocus blur detection, there are still several unsolved challenges, e.g., interference of background clutter, scale sensitivity and missing boundary details of blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We first fuse the features from different layers of FCN as shallow features and semantic features, respectively. Then, the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions, and the fused semantic features are propagated to shallow layers to assist in better locating blur regions. The fusion and refinement are carried out recurrently. In order to narrow the gap between low-level and high-level features, we embed a feature adaptation module before feature propagating to exploit the complementary information as well as reduce the contradictory response of different feature layers. Since different feature channels are with different extents of discrimination for detecting blur regions, we design a channel attention module to select discriminative features for feature refinement. Finally, the output of each layer at last recurrent step are fused to obtain the final result. We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study. Extensive experiments on two commonly used datasets and our newly collected one are conducted to demonstrate both the efficacy and efficiency of DeFusionNet.

Current spatial-keyword publish/subscribe systems need to handle spatial-keyword skyline queries over geo-textual streams to continuously obtain good results. The skyline queries in such systems face two main problems: (1) query problems, because the powerful query capability is required for the strict limit of the response time and the large number of items concerned by the users, and (2) scalability issue, because millions of active users are maintained simultaneously with many network-connected machines. Unfortunately, the current approach is towards static data. Thus, this paper first proposes a distributed skyline query processing framework. Then, we optimize the skyline computing by introducing MF-R$^t$-tree, which is an update-efficient and space-saving indexing structure and a fast approach for processing a continuous spatial-keyword skyline query called$eager^*$. Finally, a spatial and textual signature-based communication optimization method is proposed to support scalability. The experimental results indicate that (1) MF-R$^t$-tree can significantly reduce update costs, while maintaining a low storage cost, and a query performance comparable to IL-Quadtree, (2)$eager^*$can averagely accelerate 79.72 × faster than the method based on BNL, (3) the communication optimization method significantly reduces the communication cost, and (4) the distributed framework can efficiently support large-scale skyline queries.

Mountain roads are a source of important basic geographic data used in various fields. The automatic extraction of road images through high-resolution remote sensing imagery using deep learning has attracted considerable attention. But the interference of context information limited extraction accuracy, especially for roads in mountain area. Furthermore, when pursuing research in a new district, many algorithms are difficult to train due to a lack of data. To address these issues, a framework based on remote sensing road-scene neighborhood probability enhancement and improved conditional generative adversarial network (NIGAN) is proposed in this article. This framework can be divided into two sections: 1) road scenes classification section. A remote sensing road-scene neighborhood confidence enhancement method was designed for classifying road scenes of the study area to reduce the impact of nonroad information on subsequent fine-road segmentation and 2) fine-road segmentation section. An improved dilated convolution module, which is helpful in extracting small objects such as road, was added into the conditional generative adversarial network (CGAN) to increase the receptive field and pay attention to global information, and segment roads from the results of road scenes classification section. To validate the NIGAN framework, new mountain road-scene and label datasets were constructed, and diverse comparison experiments were performed. The results indicate that the NIGAN framework can improve the integrity and accuracy of mountain road-scene extraction in diverse and complex conditions. The results further confirm the validity of the NIGAN framework in small samples. In addition, the mountain road-scene datasets can serve as benchmark datasets for studying mountain road extraction.

Urban informal settlements (UIS) are high-density population settlements with low standards of living and supply. UIS semantic segmentation, which identifies pixels corresponding to informal settlements in remote sensing images, is crucial to the estimation of poor communities, urban management, resource allocation, and future planning, particularly in megacities. However, most studies on informal settlement mapping are either based on parcels (image classification) or pixels (semantic segmentation). Few studies utilize object information to improve UIS mapping. Since informal settlements are formed by buildings (objects), utilizing object information can improve UIS semantic segmentation. Furthermore, current UIS mapping studies mainly focus on using single-modality remote sensing images, and there is a lack of related research on using multimodal data. Due to the spatial heterogeneity of informal settlements, using only a single modality of remote sensing image features limits the effectiveness and accuracy of informal settlements semantic segmentation. Aiming at achieving fine-scale UIS mapping results, this paper proposes a UIS semantic segmentation method, namely UisNet, that utilizes a transformer-based block to receive multimodal data, including high-spatial-resolution remote sensing images (parcel- and pixel-level) and building polygon data (object-level) to identify UIS. The experiments were conducted in Shenzhen City, and they confirmed the superior performance of UisNet, which achieved an overall accuracy (OA) of 94.80% and a mean intersection over union (mIoU) of 85.51% in the testing set of the manually labeled UIS semantic segmentation dataset (UIS-Shenzhen dataset) and outperformed the best models on semantic segmentation tasks. Besides, we add a set of experiments on a public dataset (GID dataset) and compare our method with the current state-of-the-art semantic segmentation methods. Experiments show that the proposed UisNet improves mIoU by 1.64% to 7.58% compared to other methods. This work will be available at https://github.com/RunyuFan/.

Urban Villages (UV) refer to areas of urban informal settlements lagging behind the rapid urbanization process. Recent studies focus on using satellite images to classify UV. However, satellite images only capture objects from a bird-eye perspective, thus cannot obtain complex spatial relationships between objects. In UV areas, buildings and objects are usually dense, small in size, and obscure each other. Therefore, it is challenging to classify UV accurately using only satellite images with bird-eye perspectives. In this paper, to solve this problem, we proposed a novel method that uses satellite images combined with streetview images to classify UV. Specifically, we propose a novel multilevel spatial-channel feature fusion network, namely FusionMixer, that integrates CNN-based feature extraction modules and a multilevel spatial-channel feature fusing layer to make an optimal UV classification. Experiments were conducted in Shenzhen City (the RsSt-ShenzhenUV dataset) and a public UV dataset (theS2UVdataset). The proposed FusionMixer achieved an increase of OA by 8.83% and 8.84%, and improves Kappa by 0.1765 and 0.1770 in the validation set and testing set, compared to the second-best fusion models in RsSt-ShenzhenUV dataset. Experiments in theS2UVdataset show that the proposed FusionMixer improves OA by 1.82% and Kappa by 0.04 compared to other methods. We also added a set of experiments on a public dataset (Houston dataset) and compare our method with the current state-of-the-art multimodal fusion methods to prove the generalization of the proposed FusionMixer in fusing other multimodality data. These experiments confirmed the superior performance of the proposed FusionMixer.

With the introduction of artificial intelligence (AI), current deep learning-driven change detection methods generally regard changes as a type of specific land cover information and try to detect them simply by using existing semantic labeling models. In practice, the changes that occur on different land cover categories may appear completely different for remote sensing images, making it difficult to detect multiple categories of changes with an end-to-end model. In addition, training such networks requires a large amount of prelabeled references, which are labor-intensive and time-consuming. Motivated by this observation, we integrate dual learning algorithm and disentangled representation theory to develop a novel approach, named content-invariant dual learning (CiDL), for either supervised or unsupervised change detection in remote sensing images. In our framework, two opposite Y-shaped networks, each of which consists of two encoders and one decoder, are introduced to translate bitemporal images from their original domains to each others, where their intrinsic content features are retained, while their style features are consistent. By training our hybrid framework, even without references, this method learns a category-wise cross-domain translation to suppress the discrepancies in paired unchanged regions, and meanwhile highlight those in paired changed regions. The experimental results on two typical change detection data sets and the comparison with other state-of-the-art deep learning-driven methods verify the effectiveness and competitiveness of our proposed CiDL.

Hyperspectral anomaly detection, which is aimed at locating anomaly, has received widespread attention. In this article, a new anomaly detector, named local spatial constraint and total variation (LSC-TV), is proposed for hyperspectral imagery. In anomaly detection methods based on low-rank representation, background pixels are usually considered to have a global low-dimensional structure. However, the complex background distribution in hyperspectral images (HSIs) means that this global low-dimensional structure rarely occurs. In LSC-TV, the effective local spatial information is extracted by superpixel segmentation, and the regularization based on the F-norm is used to force the background within the same superpixel to show uniform spectral features. Moreover, each pixel is given a penalty based on the degree of anomaly determined during model iteration, while the anomaly is not considered by the background constraint. In addition, the background pixels in the neighborhood often show a high correlation, whereas the anomaly does not possess this feature. Nonisotropic TV is introduced into the proposed LSC model using the correlation of first-order neighborhoods to make it easier for anomalies to be separated. The proposed LSC-TV method and current state-of-the-art methods are tested on a set of simulated data and four sets of real data. The experimental results demonstrate that the proposed method is superior to the comparative method in terms of both color map detection and quantitative evaluation.

Geological remote sensing interpretation can extract elements of interest from multiple types of images, which is vital in geological survey and mapping, especially in inaccessible regions. However, due to numerous classes, high interclass similarities, complex distributions, and sample imbalances of geological elements, the interpretation results of machine-learning (ML)-based methods are understandably worse than manual visual interpretation. Additionally, scholars in remote sensing have mainly carried out their works to interpret a single geological element category, such as mineral, lithological, soil and structure. The interpretation of multiple geological elements is missing, which is more in line with the open world. To improve the interpretation results of ML-based methods and reduce the labor cost in geological survey and mapping, we propose a deep-learning (DL)-feature-based adaptive multi-source data fusion network (AMSDFNet) for the efficient interpretation of multiple geological remote sensing elements. The AMSDFNet has two branches for learning valuable spatial and spectral information from two kinds of data sources, wherein the atrous spatial pyramid pooling operation and an attention block are applied to adaptively extract and fuse multi-scale informative features. A hard example mining algorithm was also added to select important training examples to address sample imbalance. A large-scale region in western China with sufficient geological elements was set as the research area. The proposed model improved the two critical metrics by more than 2% in the experiment section. As far as we know, this research work is the first time DL features and multi-source remote sensing images have been utilized to simultaneously interpret geological elements of lithology, soil, surface water, and glaciers. The extensive experimental results demonstrated the superiority of DL features and our model in geological remote sensing interpretation.

Land surface temperature (LST) is widely used in the field of time-series remote sensing. However, due to the influence of cloud cover, the large area of LST data observation is missing, which seriously affects the later data analysis. In the past research, various effective interpolation methods have been developed, but they usually cannot effectively interpolate the image data with large observation missing. In this article, a new method for interpolating these missing data called Hilbert tensor rearrangement with Bayesian temporal tensor factorization (HTR-BTTF) is proposed. This method requires tensor rearrangement of remote sensing data, combined with BTTF method for interpolation. In order to evaluate the performance of our method, we select three real study areas with different climates, Wuhan, Harbin, and Kunming LST data during day and night, and add cloud covers with different sizes to the cold and warm season layers each year. BTTF, inverse distance weighted (IDW), harmonic analysis of time series (HANTS), and GapFill are used as comparison methods. Root-mean-square error (RMSE) is a comprehensive evaluation index of interpolation results. Experiments have shown that HTR-BTTF is an effective method for interpolating missing observations, which is better than other methods. In the simulation experiment of the largest cloud cover size, on average, the RMSE of the data filled using the HTR-BTTF method was 17.2% lower than that of BTTF and 54.9% lower than that of the GapFill method, and it shows good robustness and high accuracy.

Remote sensing change detection (RSCD), which identifies the changed and unchanged pixels from a registered pair of remote sensing images, has enjoyed remarkable success recently. However, locating changed objects with fine structural details is still a challenging problem in RSCD. In this paper, we propose a novel remote sensing change detection network via temporal feature interaction and guided refinement (TFI-GR) to solve this issue. Specifically, unlike previous methods, which just employ one single concatenation or subtraction operation for bi-temporal feature fusion, we design a temporal feature interaction module (TFIM) to enhance interaction between bi-temporal features and capture temporal difference information at diverse feature levels. Afterword, a guided refinement modules (GRM), which aggregates both low- and high-level temporal difference representations to polish the location information of high-level features and filter the background clutters of low-level features, is repeatedly performed. Finally, the multi-level temporal difference features are progressively fused to generate change maps for change detection. To demonstrate the effectiveness of the proposed TFI-GR, comprehensive experiments are performed on three high spatial resolution remote sensing change detection datasets. Experimental results indicate that the proposed method is superior to other state-of-the-art change detection methods. The demo code of this work is publicly available at https://github.com/guanyuezhen/TFI-GR.

High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image interpretation, and single image super-resolution (SISR) reconstruction technology is becoming increasingly valuable and significant. The state-of-the-art deep-learning-based SISR methods have demonstrated remarkable advantages, while reconstructing complex texture details still remains a big challenge. Besides, as a typical ill-posed inverse problem, how to determine the optimal solution is another important topic. To address these problems, in this work, a dual learning-based graph neural network (DLGNN) is proposed, in which the GNN is utilized to consider the self-similarity patches in remote sensing imagery by aggregating cross-scale neighboring feature patches, and dual learning strategy is adopted to refine the reconstruction results by constraining the mapping process in terms of the loss function, transferring the typical ill-posed problem to a well-posed one. Abundant experiments on 3K VEHICLE_SR datasets and Massachusetts Roads demonstrate the validity and outstanding performance for remote sensing image super-resolution tasks compared with other state-of-the-art super-resolution construction methods. Code is available at https://github.com/CUG-RS/DLGNN.

Nowadays, a variety of different sources can be combined together to measure and monitor maritime human activities. Reliable data fusion techniques are essential to associate the targets from different systems for maritime surveillance. In particular, the fusion of data from Sentinel-2 satellites and the Automatic Identification System (AIS) has attracted wide attention due to their public availability and complementarity. However, most traditional methods for target association are not suitable for this particular case, due to the time lag phenomenon of Sentinel-2 data. In this study, we first construct two new datasets for the detection of moving ships and their wakes based on Sentinel-2 images. Combined with the detection results obtained by the You Only Look Once (YOLOv5) model, the position and course information of the detected ships are first extracted. After carefully analyzing the time lag phenomenon of Sentinel-2 data, we develop a new domain adaptation-based method for target association based on the fusion of Sentinel-2 and AIS data, called Moving Ship Optimal Association (MSOA). Different from standard domain adaptation methods only for representation alignment, the proposed MSOA is able to align representation, time and position simultaneously. A case study is provided in which the newly proposed method is tested over the Port of Long Beach, USA. Experimental results demonstrate that both moving ships and wakes are well detected. Specifically, our newly proposed MSOA exhibits more accurate and robust performance when compared to traditional methods, and the detected ships without corresponding AIS tracks can also be detected by our MSOA. Moreover, the real sensing time and time lag of Sentinel-2 data are deduced with high accuracy. Overall, it can be concluded that our MSOA provides a new perspective for accurate target association based on heterogeneous data fusion.

Due to the limitation of technology and budget, it is often difficult for sensors of a single remote sensing satellite to have both high temporal resolution and high spatial (HTHS) resolution at the same time. In this paper, we proposed a new Multi-level Feature Fusion with Generative Adversarial Network (MLFF-GAN) for generating fusion HTHS images. MLFF-GAN mainly uses U-net-like architecture and its generator is composed of three stages: feature extraction, feature fusion, and image reconstruction. In feature extraction and reconstruction stage, the generator employs the encoding and decoding structure to extract three groups of multi-level features, which can cope with the huge difference of resolution between high-resolution images and low-resolution images. In the feature fusion stage, Adaptive Instance Normalization (AdaIN) block is designed to learn the global distribution relationship between multi-temporal images, and an attention module (AM) is used to learn the local information weights for the change of small areas. The proposed MLFF-GAN was tested on two Landsat and MODIS datasets. Some state-of-the-art algorithms are comprehensively compared with MLFF-GAN. We also carried on the ablation experiment to test the effectiveness of different sub-module in MLFF-GAN. The experiment results and ablation analysis show the better performances of the proposed method when compared with other methods. The code is available at https://github.com/songbingze/MLFF-GAN.

Dense time-series remote sensing images have transformed the traditional bitemporal land-cover change detection to continuous monitoring. Previous work mostly employs linear fitting, prediction, or decomposition methods, and the detection accuracy is not high. The latest progress of deep learning (DL) shows its advantages in time-series change monitoring. However, DL models are computationally expensive and require lots of labeled samples, resulting in often employed prediction-threshold-based unsupervised change detection method. However, the determination of a reasonable threshold has always been a big problem. Therefore, we proposed the similarity-measurement-based deep transfer learning for time-series adaptive change detection (SDTL-TSACD) model. First, a standard dynamic time warping (SDTW) distance was proposed and used to cluster large-scale time series into multiple subcategories with high time-series similarity. Second, a time convolutional network (TCN) was used for nonlinear time-series fitting and prediction, and an early stop strategy was used to prevent overfitting. Then, the trained TCN model would be transferred and performed pixel-by-pixel time-series prediction within the same category, and the SDTW was also used to evaluate the prediction accuracy. Finally, the Otsu adaptive threshold was used to detect change points, and the spatial neighbor relationship was used to eliminate the pseudo-change points. Change detection results using 132 benchmark datasets showed that the SDTL-TSACD performed well in both accuracy and efficiency. In addition, the MOD13Q1-EVI images from 2001 to 2020 were used to study the land-cover change of the Loess Plateau, and the SDTL-TSACD also showed a good ability to solve practical problems.

Road information from high-resolution remote-sensing images is widely used in various fields, and deep-learning-based methods have effectively shown high road-extraction performance. However, for the detection of roads sealed with tarmac, or covered by trees in high-resolution remote-sensing images, some challenges still limit the accuracy of extraction: 1) large intraclass differences between roads and unclear interclass differences between urban objects, especially roads and buildings; 2) roads occluded by trees, shadows, and buildings are difficult to extract; and 3) lack of high-precision remote-sensing datasets for roads. To increase the accuracy of road extraction from high-resolution remote-sensing images, we propose a split depth-wise (DW) separable graph convolutional network (SGCN). First, we split DW-separable convolution to obtain channel and spatial features, to enhance the expression ability of road features. Thereafter, we present a graph convolutional network to capture global contextual road information in channel and spatial features. The Sobel gradient operator is used to construct an adjacency matrix of the feature graph. A total of 13 deep-learning networks were used on the Massachusetts roads dataset and nine on our self-constructed mountain road dataset, for comparison with our proposed SGCN. Our model achieved a mean intersection over union (mIOU) of 81.65% with an F1-score of 78.99% for the Massachusetts roads dataset, and an mIOU of 62.45% with an F1-score of 45.06% for our proposed dataset. The visualization results showed that SGCN performs better in extracting covered and tiny roads and is able to effectively extract roads from high-resolution remote-sensing images.

High spatial resolution (HSR) remote sensing image scene classification is a crucial way for land-use interpretation. However, most of the current scene classification methods assume that the training and test sets of remote sensing images follow the same feature distribution. In practical application, this assumption is difficult to guarantee. Domain adaptation (DA) is a machine learning paradigm that can effectively alleviate such problems. However, previous works mostly focused on aligning the global distribution of source domain (SD) and target domain (TD), which lose the inter-subdomain contextual relations between both domains, and ignore the redundancy among features. However, most DA methods usually only use the manually designed measurement criteria to establish the relationship between the SD and the TD, which is insufficient or complicated. In this paper, a weakly pseudo-supervised decorrelated subdomain adaptation (WPS-DSA) framework is proposed for HSR cross-domain land-use classification. In WPS-DSA, a feature extractor based on the subdomain adaptation network is used to extract the inter-subdomain characteristics of both domains. To weaken the influence of the features redundancy among remote sensing images, the switchable whitening module is introduced. In addition, a domain hierarchical sampling mechanism is designed to strengthen the connection between SD and TD in a simple way. Moreover, the WH-SH DA Dataset which is sampled from two typical Chinese cities is constructed to verify the generalization of the proposed framework. The experimental results of the cross-domain tasks on three publicly available HSR datasets and WH-SH DA Dataset display considerable performance and generalization ability of WPS-DSA.

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the$l_{2,1}$-norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.

Hyperspectral unmixing is an essential task in hyperspectral imagery applications. Because of the strong feature extract ability and satisfying performance, deep learning methods have been used for hyperspectral unmixing. However, there are still several problems in existing deep learning based spectral unmixing methods. Supervised learning methods can only accomplish a single task and lack a large amount of data for supervised learning. While the unsupervised learning unmixing methods are easily misled by the traditional way of initialization. In this paper, a weakly supervised deep convolutional neural network is proposed for hyperspectral unmixing. The experimental results show that competitive results can also be obtained by pretraining with a small number of samples, and weakly supervised learning still has potential for hyperspectral unmixing.

Extracting roads from remote sensing images has both civilian and military value, such as GIS data update, road navigation, military command and so on. The existing road extraction methods are mainly based on fully convolutional neural networks, and have achieved the state-of-the-art results. However, the convolutional and deconvolutional forms of these methods destroy the completeness of the extracted road. In this paper, we present a novel road extraction method for extracting complete roads from high-resolution remote sensing imagery based on joint superpixel segmentation and Graph Convolutional Network(GCN). The proposed method retains more spatial detail information as well as effectively improves the integrity of the extracted roads. Experiments were conducted on the Massachusetts Road dataset to compare our proposed method to other commonly used full convolutional techniques for road extraction. The results demonstrated the validity and better performance of the proposed method.

Recently, hyperspectral anomaly detection methods based on low-rank representation(LRR) have been widely studied. However, the assumption of global low dimension of background may ignore the local structure information of hyperspectral image. In this paper, a novel LRR incorporating local spatial constraint method is proposed for hyperspectral anomaly detection. Different from LRR detector, the proposed method considers the spatial information based on the supe pixel in the background part. The proposed method and current state-of-the-art methods are tested on two sets of real data. The experimental results demonstrate that the proposed method is superior to the comparative method in terms of both colour map detection and quantitative evaluation.

Timely and effective quantitative measurement of enterprises' offline resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this paper, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-influenced Chinese Spring Festival in 2020 with National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data. The results demonstrate that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since February 10th, work has been resuming in localities. By late March, the work resumption indexes of most cities exceeded 50%, and Shanghai and Nanjing even had achieved complete resumption of work. Our method effectively estimates the resumption of work, which provides a scientific basis for local governments to formulate subsequent resumption policies.

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

Regarding landslide inventory mapping (LIM) as a task similar to change detection, current methods for LIM using bi-temporal optical remote sensing images are generally derived from change detection methods. In practice, not all changed regions belong to landslides, e.g., new roads, canals, and vegetation. Therefore, an ideal strategy is supposed to present two steps: discriminating changed and unchanged regions, and detecting landslides apart from other changed regions. Owing to the complexity and uncertainty of landslides, it is difficult to simultaneously separate landslides with unchanged and other changed regions by a single model. Addressing this problem, in this letter, we apply a generative adversarial network (GAN) in a Siamese neural network, and then propose a GAN-based Siamese framework (GSF) for LIM. The GSF comprises two cascaded modules, namely, domain adaptation and landslide detection. The former module aims to make a cross-domain mapping between prelandslide and postlandslide images with adversarial learning, then translate paired images into the same domain to suppress the domain discrepancies of bi-temporal remote sensing images. Meanwhile, the latter module aims to perform pixel-level landslide detection with a Siamese model. By training this cascaded framework, our method learns to produce landslide inventory maps without any preprocessing or postprocessing. Extensive experiments and comparison with other state-of-the-art methods verify the efficiency and superiority of our method.

Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges, distributed computing is increasingly viewed as a feasible solution to parallelize the analysis of massive data coming from different sources (e.g., remote sensing and social media data). In this article, we provide an overview of available and new distributed strategies to address the computational challenges brought by massive heterogeneous data processing and fusion for real-time environmental monitoring and decision-making. The 2013 Boulder (Colorado) flood event is taken as a case study to evaluate several new distributed data fusion frameworks. Experimental results demonstrate that the proposed distributed frameworks are suitable in terms of response time and computational requirements for fusing large-volume heterogeneous data sources.

A simplified approach to accelerate matrix factorization of big data is to parallelize it. A commonly used method is to divide the matrix into multiple non-intersecting blocks and concurrently calculate them. This operation causes the Load balance problem, which significantly impacts parallel performance and is a big concern. A general belief is that the load balance across blocks is impossible by balancing rows and columns separately. We challenge the belief by proposing an approach of “Balanced Partitioning (BaPa)”. We demonstrate under what circumstance independently balancing rows and columns can lead to the balanced intersection of rows and columns, why, and how. We formally prove the feasibility of BaPa by observing the variance of rating numbers across blocks, and empirically validate its soundness by applying it to two standard parallel matrix factorization algorithms, DSGD and CCD++. Besides, we establish a mathematical model of “Imbalance Degree” to explain further why BaPa works well. BaPa is applied to synchronous parallel matrix factorization, but as a general load balance solution, it has significant application potential.

Due to the trade-off of temporal resolution and spatial resolution, spatiotemporal image-fusion uses existing high-spatial-low-temporal (HSLT) and high-temporal-low-spatial (HTLS) images as prior knowledge to reconstruct high-temporal-high-spatial (HTHS) images. However, some existing spatiotemporal image-fusion algorithms ignore the issue that the spatial information of HTLS images is insufficient to support the acquisition of spatial information, which leads to the unsatisfactory accuracy of the fusion result. To introduce more spatial information, the algorithm in this article uses Cycle-generative adversarial networks (GANs) to simulate the change process of two HSLT images at k-1 and k+1, and to generate some simulated images between k-1 and k+1. Then, the generated images are selected under the help of HTLS images, and the selected ones are then enhanced with wavelet transform. Finally, the image with spatial information is introduced into the Flexible Spatiotemporal DAta Fusion (FSDAF) framework to improve the performance of spatiotemporal image-fusion. Extensive experiments on two real data sets demonstrate that our proposed method outperforms current state-of-the-art spatiotemporal image-fusion methods.

Object detection, aiming to recognize and locate objects of interest in aerial images, has historically played a significant role in the remote sensing community. Following remarkable improvements in Earth observation technologies, high-resolution remote sensing (HRRS) images with a bird’s eye view perspective have revealed many categories of objects with sufficient variations in appearance and on complex backgrounds that make HRRS object detection an active but challenging task. The selection of positive samples and negative training instances is an essential factor in influencing detectors’ performance. Related studies have found that many low-quality negative samples in the detectors’ training process have caused training instability and low detection accuracy. In this work, a novel sampling-balance-based multistage network (SB-MSN) is presented to adaptively mine high-quality positive and negative instances for training an accurate detector. It has a series of components to ensure the selection and generation of high-quality examples for training an accurate detector, including a multiscale information retention module, an intersection over union balance sampling strategy, a balance L1 loss, and a multistage network. The proposed detector has been evaluated on three representative HRRS data sets. The extensive experimental results show that our detector can solve the problem of low-quality samples and significantly improve the detection performance of the mAP by 1.4% with the NWPU VHR-10 data set, 3.5% with the high-resolution remote sensing detection (HRRSD) data set, and 4.2% with the detection in the optical remote (DIOR) data set.1

Sparse unmixing, as a semisupervised unmixing method, has attracted extensive attention. The process of sparse unmixing involves treating the mixed pixels of hyperspectral imagery as a linear combination of a small number of spectral signatures (endmembers) in a standard spectral library, associated with fractional abundances. Over the past ten years, to achieve a better performance, sparse unmixing algorithms have begun to focus on the spatial information of hyperspectral images. However, less accurate spatial information greatly limits the performance of the spatial-regularization-based sparse unmixing algorithms. In this article, to overcome this limitation and obtain more reliable spatial information, a novel sparse unmixing algorithm named superpixel-based reweighted low-rank and total variation (SUSRLR-TV) is proposed to enhance the performance of the traditional spatial-regularization-based sparse unmixing approaches. In the proposed approach, superpixel segmentation is adopted to consider both the spatial proximity and the spectral similarity. In addition, a low-rank constraint is enforced on the objective function as pixels within each superpixel have the same endmembers and similar abundance values, and they naturally satisfy the low-rank constraint. Differing from the traditional nuclear norm, a reweighted nuclear norm is used to achieve a more efficient and accurate low-rank constraint. Meanwhile, low-rank consideration is also used to enhance the spatial continuity and suppress the effects of random noise. Furthermore, TV regularization is introduced to promote the smoothness of the abundance maps. Experiments on three simulated data sets, as well as a well-known real hyperspectral imagery data set, confirm the superior performance of the proposed method in both the qualitative assessment and the quantitative evaluation, compared with the state-of-the-art sparse unmixing methods.

The fusion of heterogeneous remote sensing and social media data can fill the gaps in satellite image collections and improve the spatiotemporal resolution of the available data sets. As a result, it is being gradually adopted in multimodal data analytics. Generally, the fusion of heterogeneous geographic data faces the following issues: 1) the probability density functions may differ from different data sources and 2) the geolocations may not be well aligned. The former one can be generally solved by performing an alignment of representations in the source and target domains using, for instance, domain adaptation. The latter issue is seldom considered in the fusion of heterogeneous geographic data. In this article, we present a new method called geographic optimal transport (GOT), which aims at aligning representations and geolocations in a simultaneous fashion. A flood event that took place in 2013 in Boulder, CO, USA, is taken as a case study to evaluate our GOT method. Here, we consider two remote sensing features derived from water indicators, i.e., the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), for the fusion of Landsat 8 imagery and Twitter data. A comparison between our newly developed GOT and the traditional optimal transport (OT) is performed. Experimental results demonstrate that the proposed GOT can accurately align spatially biased georeferenced tweets to the flood phenomena, leading to the conclusion that GOT can effectively fuse heterogeneous remote sensing and social media data.

Extracting the latent factors of big time series data is an important means to examine the dynamic complex systems under observation. These low-dimensional and “small” representations reveal the key insights to the overall mechanisms, which can otherwise be obscured by the notoriously high dimensionality and scale of big data as well as the enormously complicated interdependencies amongst data elements. However, grand challenges still remain: (1) to incrementally derive the multi-mode factors of the augmenting big data and (2) to achieve this goal under the circumstance of insufficient a priori knowledge. This study develops an incrementally parallel factorization solution (namely I-PARAFAC) for huge augmenting tensors (multi-way arrays) consisting of three phases over a cutting-edge GPU cluster: in the “giant-step” phase, a variational Bayesian inference (VBI) model estimates the distribution of the close neighborhood of each factor in a high confidence level without the need for a priori knowledge of the tensor or problem domain; in the “baby-step” phase, a massively parallel Fast-HALS algorithm (namely G-HALS) has been developed to derive the accurate subfactors of each subtensor on the basis of the initial factors; in the final fusion phase, I-PARAFAC fuses the known factors of the original tensor and those accurate subfactors of the “increment” to achieve the final full factors. Experimental results indicate that: (1) the VBI model enables a blind factor approximation, where the distribution of the close neighborhood of each final factor can be quickly derived (10 iterations for the test case). As a result, the model of a low time complexity significantly accelerates the derivation of the final accurate factors and lowers the risks of errors; (2) I-PARAFAC significantly outperforms even the latest high performance counterpart when handling augmenting tensors, e.g., the increased overhead is only proportional to the increment while the latter has to repeatedly factorize the whole tensor, and the overhead in fusing subfactors is always minimal; (3) I-PARAFAC can factorize a huge tensor (volume up to 500 TB over 50 nodes) as a whole with the capability several magnitudes higher than conventional methods, and the runtime is in the order of 1/n to the number of compute nodes; (4) I-PARAFAC supports correct factorization-based analysis of a real 4-order EEG dataset captured from a variety of epilepsy patients. Overall, it should also be noted that counterpart methods have to derive the whole tensor from the scratch if the tensor is augmented in any dimension; as a contrast, the I-PARAFAC framework only needs to incrementally compute the full factors of the huge augmented tensor.

Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyper-parameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a unified graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the first attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The final unified graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.

Defocus blur detection aims to separate the in-focus and out-of-focus regions in an image. Although attracting more and more attention due to its remarkable potential applications, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. In order to address these issues, we propose a deep neural network which Recurrently Refines Multi-scale Residual Features (R2MRF) for defocus blur detection. We firstly extract multi-scale deep features by utilizing a fully convolutional network. For each layer, we design a novel recurrent residual refinement branch embedded with multiple residual refinement modules (RRMs) to more accurately detect blur regions from the input image. Considering that the features from bottom layers are able to capture rich low-level features for details preservation while the features from top layers are capable of characterizing the semantic information for locating blur regions, we aggregate the deep features from different layers to learn the residual between the intermediate prediction and the ground truth for each recurrent step in each residual refinement branch. Since the defocus degree is sensitive to image scales, we finally fuse the side output of each branch to obtain the final blur detection map. We evaluate the proposed network on two commonly used defocus blur detection benchmark datasets by comparing it with other 11 state-of-the-art methods. Extensive experimental results with ablation studies demonstrate that R2MRF consistently and significantly outperforms the competitors in terms of both efficiency and accuracy.

Hyperspectral unmixing is an essential task in hyperspectral imagery applications. Deep learning methods have been taken into hyperspectral unmixing because of its great feature extraction ability and better performance. However, there are several problems in existing deep learning based spectral unmixing methods. The networks are not deep enough to exploit their feature extraction capabilities in these unsupervised autoencoders based methods, and their effects are not stable. The main reason may be the limited prior information limited the ability of conducting the supervised method. In this manuscript, a semi-supervised deep learning based unmixing method is proposed. Unlike the existing methods, our model uses deeper neural networks without pooling layers, and the endmember spectrum are selected supervised from the original data, which uses nature and nurture cooperatively. The experimental results show that the proposed method achieves better performance and produces more accurate abundance maps, as well as higher quantitative results, compared with the current state-of-the-art deep learning unmixing algorithms.

Spatiotemporal fusion utilizes the complementarity of high-temporal-low-spatial (HTLS) and high-spatial-low-temporal (HSLT) resolution data to obtain high temporal and spatial (HTHS) resolution fusion data, which can effectively satisfy the demand for HTHS data. However, due to the difference of spatial resolution, it is difficult to obtain precise spatial information in spatiotemporal fusion. To solve this problem, a multi-level strategy-based spatial domain prediction algorithm is proposed to enhance the spatial information extraction in spatiotemporal remote sensing imagery fusion, which can reduce the noise superposition in the process of multiple reconstruction. By learning-based first and then interpolation-based Super resolution reconstruction, the proposed method can obtain better prediction of spatial information and improve the accuracy of spatiotemporal fusion.

Urban land use and land cover (LULC) change is the result of urban population economic activities and national policy. Determining the spatial pattern of land cover types in cities is of particular significance for regional sustainable development. To achieve a better understand the spatiotemporal patterns of land use types in Shenzhen, the fractal dimension of spatial distributions is adopted as an index of the complex evolution of urban land-use. In addition, a long-term sequences LULC datasets are collected to do analysis, which covers the period 1988-2015 by employing Landsat TM/ETM+/OLI of 1988, 1993, 1999, 2001, 2005, 2008, 2011, 2013 and 2015. Last but not least, a granularity analysis is adopted to study the structural changes of each land cover. After analysis, it can be observed that a significant self-similarity law exists in the Shenzhen city. From 1988 to 2015, the fractal dimension of grassland, waterbody and bare land exhibits a bi-fractals dimension. However, grassland and bare land structure show a bi-fractals trend which increases every year, and the water-body bi-fractals trend is weakening. The development of urban land in this region experiences a process of a multiscale differential development with a hierarchical spatial system. These findings will provide some scientific references for the regional planning decisions on evolution of urban land use of Shenzhen city.

Focusing on the problems of insufficient high-quality training samples to conduct an ideal detector for high-resolution remote sensing (HRRS) image object, we applied a multi-stage based detector to apply a resampling progressively strategy, which guarantees the amount of the positive training set and minimizing overfitting. The method has a sequence of regression heads training on the samples chosen by different Intersection over Union (IoU) thresholds. The first head with a low IoU threshold trained by a large number of positive samples and can prepare more high-quality samples for the remaining branches. The subsequent heads with the increasing IoU thresholds would train on more abundant positive samples and to conduct an accurate detector and avoid the problem of overfitting. The proposed method reached the best mAP value and outperformed the comparison methods by about 10%. The experimental results show that our method can significantly improve detection performance and solve the problem of lacking high-quality samples.

Remote sensing scene classification is a challenging task due to the complex high-resolution textures and similarity within and variations between classes. The high-resolution network (HRN et) is introduced into this application considering its capability of high-resolution and multi-scale semantic representations. Moreover, squeeze-and-excitation (SE) attention mechanism is employed to enhance and suppress features based on their interdependence between channels. By fusing SE block into HRNet, the proposed SE-HRNet can better distinguish scene categories with enriched features. Experimental results on the NWPU-RESIS45 and AID data sets validate the state-of-the-art performance of the proposed SE-HRNet.

Inshore ship detection is a challenging task due to the complex background and object placement in remote sensing port images. To address this problem, we propose a detection method based on semantic and instance segmentation. First, a multi-information fusion network is designed to segment ship objects by taking edge and global information into account, which comprises a multi-task network, a global network and a fusion network. Then a simple but effective instance segmentation method based on line scanning and interval analysis is presented to separate multiple ships within one semantic region. Experimental results on an inshore ship data set collected from Google Earth validate the state-of-the-art performance of the proposed method.

Storm surge hazard annually costs hundreds of lives and billions of dollars for damages in coastal countries. Simulating the storm surge events and alerting flood areas in advance is essential to guarantee the safety of citizens and urban infrastructure. However, due to the complexity of the urban environment, the accuracy of simulation faces challenges. By leveraging the facility of the Internet-of-Things (IoT) technique, the urban flood simulation procedure under storm surge hazard can be improved. In this article, targeting a coastal city usually under the threat of the storm surge, a deep-reinforcement-learning-based storm surge flood simulation approach is proposed to simulate the flood situation under the specific level of the storm surge. By analyzing real-time urban flood data and weather data collected by the IoT system, the model of the urban flood can be constructed and refined. A case study based on storm surge took place on July 1, 2019 in Shenzhen and was conducted to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed method can effectively simulate the storm surge flood, and the achieved accuracy is up to 97.22%.

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big services, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation and experimental results from real-life, the high-efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social system data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients' tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.

High-resolution remote sensing (HRRS) image scene classification has attracted an enormous amount of attention due to its wide application in a range of tasks. Due to the rapid development of deep learning (DL), models based on convolutional neural network (CNN) have made competitive achievements on HRRS image scene classification because of the excellent representation capacity of DL. The scene labels of HRRS images extremely depend on the combination of global information and information from key regions or locations. However, most existing models based on CNN tend only to represent the global features of images or overstate local information capturing from key regions or locations, which may confuse different categories. To address this issue, a key region or location capturing method called key filter bank (KFB) is proposed in this article, and KFB can retain global information at the same time. This method can combine with different CNN models to improve the performance of HRRS imagery scene classification. Moreover, for the convenience of practical tasks, an end-to-end model called KFBNet where KFB combined with DenseNet-121 is proposed to compare the performance with existing models. This model is evaluated on public benchmark data sets, and the proposed model makes better performance on benchmarks than the state-of-the-art methods.

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping; plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots; second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845; and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping; the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft's simple encrypted arithmetic library (SEAL).

Feature extraction and feature selection have been regarded as two independent dimensionality reduction methods in most of the existing literature. In this paper, we propose to integrate both approaches into a unified framework and design an unsupervised linear feature selective projection (FSP) for feature extraction with low-rank embedding and dual Laplacian regularization, with the aim to exploit the intrinsic relationship among data and suppress the impact of noise. Specifically, a projection matrix with an l2,1-norm regularization is introduced to project original high dimensional data points into a new subspace with lower dimension, where the l2,1-norm regularization can endow the projection with good interpretability. We deploy a coefficient matrix with low rank constraint to reconstruct the data points and the l2,1-norm is imposed to regularize the data reconstruction errors in the low-dimensional subspace and make FSP robust to noise. Furthermore, a dual graph Laplacian regularization term is imposed on the low dimensional data and data reconstruction matrix for preserving the local manifold geometrical structure of data. Finally, an alternatively iterative algorithm is carefully designed for solving the proposed optimization model. Theoretical convergence and computational complexity analysis of the algorithm are also provided. Comprehensive experiments on various benchmark datasets have been carried out to evaluate the performance of the proposed FSP. As indicated, our algorithm significantly outperforms other state-of-the-art methods for feature extraction.

The emerging of machine learning has massively promoted the abilities of computational sustainability in natural resource management and allocation. Many Internet giants such as Google, Amazon, and Microsoft now provide Machine Learning as a Service (MLaaS) to meet the increasing demand for machine learning services. However, the prediction results of training data and testing data with the same machine learning model in MLaaS have remarkable differences, and thus the attackers can leverage machine learning techniques to launch the so-called membership inference attacks, i.e., to infer whether a record is in the training data or not. In this paper, we propose MIASec that can guarantee the data indistinguishability of the training data and thereby has the ability to defend against membership inference attacks in MLaaS. The key idea of MIASec is to narrow the dynamic ranges of vital features in the training data, such that the training data, the testing data, and even the synthetic data have almost semblable prediction results by the same machine learning model. With elaborated design on modifying the values of vital features in the training data, MIASec can thus reduce the differences between the model's outcomes of training data and testing data, thereby protecting the training data in effect while keeping the model's accuracy stable. We empirically evaluate MIASec on machine learning models trained by off-line neural networks and on-line MLaaS. Using realistic data and classification tasks, our experiment results show that MIASec can defend the membership inference attacks effectively. In particular, MIASec can reduce the precision and recall of attacks respectively by 11.7 and 15.4 percent in average, and by 18.6 and 21.8 percent at best.

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting more and more attention due to its widespread applications, defocus blur detection still confronts several challenges such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We firstly utilize a fully convolutional network to extract multi-scale deep features. The features from bottom layers are able to capture rich low-level features for details preservation, while the features from top layers can characterize the semantic information to locate blur regions. These features from different layers are fused as shallow features and semantic features, respectively. After that, the fused shallow features are propagated to top layers for refining the fine details of detected defocus blur regions, and the fused semantic features are propagated to bottom layers to assist in better locating the defocus regions. The feature fusing and refining are carried out in a recurrent manner. Also, we finally fuse the output of each layer at the last recurrent step to obtain the final defocus blur map by considering the sensitivity to scales of the defocus degree. Experiments on two commonly used defocus blur detection benchmark datasets are conducted to demonstrate the superority of DeFusionNet when compared with other 10 competitors. Code and more results can be found at: http://tangchang.net.

High-resolution remote sensing (HRRS) image scene classification has been a critical task and greatly important for many applications, wherein convolutional neural network (CNN)-based methods have achieved considerable improvements. However, the CNN-based methods have countered a severe problem that massive annotation samples are required to obtain ideal model for scene classification. There is no dataset with a comparative scale to ImageNet to meet the sample requirement and labelling samples is labor-intensive and time-consuming. To solve the problem of insufficient annotation samples, a new generative adversarial network (GAN)-based sample generation method for scene classification is implemented. The proposed method is able to generate HRRS images with specific label and improve scene classification performance for the CNN-based methods.

Sea-land segmentation is a key step in inshore ship detection and coast monitoring. Among the state-of-art segmentation approaches, semantic segmentation networks show great potential on this task, but there is still room for improvement. In this paper, we propose a method based on UNet for sea-land segmentation. We replace its contraction part with ResNet which specializes in handling complicated scenes, and construct a new network structure Res-UNet. After preliminary segmentation results are obtained, the fully connected Conditional Random Field (CRF) model and morphological operation are then used as post-processing to obtain more precise coastlines and intact regions. We test our model on a dataset collected from Google Earth and the inspiring results validate the effectiveness of our method.

Remote sensing image scene classification, which aims to identify the types of land cover, is a fundamental task in remote sensing image analysis. Remote sensing images contain a variety of land-cover objects. These land-cover objects form a complex and diverse scene through spatial combination and correlation, which makes remote sensing imagery scenes classification difficult. In addition, remote sensing images contain redundant information that has a negative impact on remote sensing imagery scene classification, which makes remote sensing imagery scenes classification rather challenging. Recently, there are many deep learning based methods, which have achieved remarkable performance through an end-to-end supervised training process. Existing advances in remote sensing imagery scene classification mainly focus on training multi-layer convolutional neural networks (CNNs). These CNNs do not explicitly distinguish between key information and redundant information of the image. Therefore, the ability to extract features is limited. How to focus on key information and ignore redundant information in remote sensing imagery scene classification is a valuable problem. Inspired by the attention mechanism, we propose a CNN-based network that combines residual units and attention mechanism. It automatically assigns large weights to key areas of the image and thus has the ability to adaptively ignore redundant information. We evaluated the proposed approach with some state-of-the-art methods on the UC Merced Land-Use dataset and the NWPU-RESISC45 dataset. Experimental results show that the proposed attention model has achieved the best classification performance.

Spatial regularization sparse unmixing (SRSU) has been widely studied and proved to be far better than the traditional spectral unmixing methods. These spatial sparse unmixing algorithms have obtained many competitive results except for the negative influences of inaccurate estimated unmixing abundances or outliers in abundances. In this paper, to obtain a more accurate SRSU results, a local block grouping with noise-adjusted principal component analysis method is used to do spatial preprocessing in sparse unmixing process. Here, local blocks are treated as a series of vector variables, and these variables are selected by grouping the pixels with similar local spatial structures to the underlying one in the local window. Then noise-adjusted principal component analysis (NAPCA) is taken to transform the original datasets into PCA domain and maintain only the most significant principal component as well as wipe off the inaccurate estimated fractional abundances. Compared with total variation-based and nonlocal means-based SRSU algorithms, the proposed joint local block grouping with NAPCA sparse unmixing method can yield competitive results with state-of-the-art spatial sparse unmixing algorithms using both simulated dataset and real hyperspectral imagery.

Reliably extracting information from satellite imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatically detecting roads. Road extraction from satellite images has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-ResUnet, which adopts U-Net structure, residual learning, and dilated convolutions for road area extraction. The network is built with ResUnet architecture and has dilated convolution layers in its center part. ResUnet architecture combines the strengths of residual units and feature concatenate, which help to ease training of networks and facilitate information propagation. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. We test our network and compare it with U-Net and ResUnet based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.

Capsule Network (CapsNet) is a brand new network structure. Aiming at limitations of Convolutional Neural Networks (CNNs), it designs capsule vector and dynamic routing to represent features and perform classification. However, though CapsNet has achieved state-of-the-art performance on simple MNIST data set, its potentials on remote sensing are not widely studied and explored. In this paper, we proposed a new network structure called Res-CapsNet to achieve remote sensing scene classification based on CapsNet. By introducing double residual modules into basic CapsNet, the capsule network is able to perform well on remote sensing images with more complex textures. Experimental results on UCMerced data set validate the effectiveness of our model, which also shows the potentials of capsule layers compared to pooling.

This research is based on Australian Data Cube Data Organization and Management Framework. Through data cleaning, data segmentation and data index, remote sensing observation data collected in Wuhan from 2000 to 2017, are organized into a data cube with time series as the Z-axis. Then, extraction of MODIS-NDVI data about 414 tile images. The mean value and standard deviation value of the tile matrix were calculated to detect the areas with frequent changes in vegetation coverage in Wuhan during the 18 consecutive years. The vegetation coverage curve was extracted using the time series as the z-axis to further explore the specific time nodes and change process of vegetation coverage. The experiment results show that: (1)During the period from 2000 to 2010, the area covered by vegetation in Wuhan decreased dramatically when the city expanded; (2)After 2010, due to the followup of green work in the later stages of urban development, the area of green space in Wuhan was restored; (3)In recent years, with the large-scale transportation projects carried out in Wuhan, to a certain extent, it has affected the existing green areas.

Based on the two-dimensional multifractional Brownian motion (2D-mFBM) scene model, a novel analytical electromagnetic scattering model considering the scattering characteristics of 2D-mFBM surface is proposed, and then a direct SAR imaging model based on 2D-mFBM is derived. The SAR images are simulated using SAR simulator based on multifractional geometric modeling and electromagnetic modeling. The multifractal spectrum and singularity power spectrum of simulated SAR images are analyzed and compared with those of 2D-mFBM scene model. The simulation and analysis indicates that the SAR image with both HH and VV polarization inherited the multifractal characteristics of 2D-mFBM terrain, and presents similar MFS with the original terrain, which provides valuable reference for the feature extraction of high-resolution SAR remote sensing image.

Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS-Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

Multilabel image annotation attracts a lot of research interest due to its practicability in multimedia and computer vision fields, while the need for a large amount of labeled training data to achieve promising performance makes it a challenging task. Fortunately, unlabeled and relevant data are widely available and these data can be used to serve the annotation task. To this end, we propose a novel adaptive hypergraph learning (AHL) method for multilabel image annotation in a semisupervised way, in which both the limited labeled data and abundant unlabeled data are utilized to facilitate the annotation performance. In detail, we seek a multilabel propagation scheme by learning a hypergraph which is used to preserve the local geometric structures of data in a high-order manner. Meanwhile, a feature projection is integrated into AHL to obtain a latent feature space where unlabeled instances can be effectively and robustly assigned with multiple labels. Experiments on six widely used image datasets are conducted to evaluate our model and the results demonstrate that the proposed AHL outperforms other state-of-the-art semisupervised methods.

With the ability to exploit the internal structure of data, graph-based models have received a lot of attention and have achieved great success in multiview subspace clustering for multimedia data. Most of the existing methods individually construct an affinity graph for each single view and fuse the result obtained from each single graph. However, the common representation shared by different views and the complementary diversity across these views are not efficiently exploited. In addition, noise and outliers are often mixed in original data, which adversely degenerate the clustering performance of many existing methods. In this paper, we propose addressing these issues by learning a joint affinity graph for multiview subspace clustering based on a low-rank representation with diversity regularization and a rank constraint. Specifically, a low-rank representation model is employed to learn a shared sample representation coefficient matrix to generate the affinity graph. At the same time, we use diversity regularization to learn the optimal weights for each view, which can suppress the redundancy and enhance the diversity among different feature views. In addition, the cluster number is used to promote affinity graph learning by using a rank constraint. The final clustering result is obtained by using normalized cuts on the learned affinity graph. An efficient algorithm based on an augmented Lagrangian multiplier with alternating direction minimization is carefully designed to solve the resulting optimization problem. Extensive experiments on various real-world datasets are conducted, and the results demonstrate well the effectiveness of the proposed algorithm.

High-resolution remote sensing (HRRS) scene classification plays an important role in numerous applications. During the past few decades, a lot of remarkable efforts have been made to develop various methods for HRRS scene classification. In this paper, focusing on the problems of complex context relationship and large differences of object scale in HRRS scene images, we propose a deep CNN-based scene classification method, which not only enables to enhance the ability of spatial representation, but adaptively recalibrates channel-wise feature responses to suppress useless feature channels. We evaluated the proposed method on a publicly large-scale dataset with several state-of-the-art convolutional neural network (CNN) models. The experimental results demonstrate that the proposed method is effective to extract high-level category features for HRRS scene classification.

As an unsupervised dimensionality reduction method, the principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image (HSI) processing and analysis tasks. It takes each band as a whole and globally extracts the most representative bands. However, different homogeneous regions correspond to different objects, whose spectral features are diverse. Therefore, it is inappropriate to carry out dimensionality reduction through a unified projection for an entire HSI. In this paper, a simple but very effective superpixelwise PCA (SuperPCA) approach is proposed to learn the intrinsic low-dimensional features of HSIs. In contrast to classical PCA models, the SuperPCA has four main properties: 1) unlike the traditional PCA method based on a whole image, the SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections; 2) most of the conventional feature extraction models cannot directly use the spatial information of HSIs, while the SuperPCA is able to incorporate the spatial context information into the unsupervised dimensionality reduction by superpixel segmentation; 3) since the regions obtained by superpixel segmentation have homogeneity, the SuperPCA can extract potential low-dimensional features even under noise; and 4) although the SuperPCA is an unsupervised method, it can achieve a competitive performance when compared with supervised approaches. The resulting features are discriminative, compact, and noise-resistant, leading to an improved HSI classification performance. Experiments on three public data sets demonstrate that the SuperPCA model significantly outperforms the conventional PCA-based dimensionality reduction baselines for HSI classification, and some state-of-the-art feature extraction approaches. The MATLAB source code is available at https://github.com/junjun-jiang/SuperPCA.

In real simulation applications, simulations often involve large volumes of three-dimensinal (3D) moving objects. With the rapid growth of the scale of simulation-problem domains, it has become a key requirement to efficiently manage massive 3D moving objects. Conventional indexing approaches for managing 3D moving objects during simulations generally sufferfrom excessive update costs. Aiming to this problem, this paper first proposes an update-efficient indexing structure by fusing a loose Octree and one update-memo structure, namely ML-Octree. ML-Octree significantly reduces the update costs of one simulation involving massive 3D moving objects. Towards providing a more efficient indexing approach, this paper has explored the feasibility of paralleling ML-Octree by employing Graphic Processing Unit (GPU). A load-balancing scheme is used to further improve the update performance of the GPU-aided ML-Octree. Finally, a distributed GPU-aided ML-Octree is proposed for large-scale simulations. The experimental results indicate that (1) ML-Octree can acquire the update-performance gain of an order of magnitude similar to that of Octree, (2) the GPU-aided ML-Octree can accelerate 5.07χ fasterthan a parallel ML-Octree with 8 CPU threads on average, (3) the load-balance scheme can improve GPU-aided ML-Octree by 2.3χ on average, and (4) the distributed GPU-aided ML-Octree can efficiently support large-scale simulations.

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to efficiently allocate Virtual Network Functions (VNF) to Virtual Machines (VMs). Although a significant amount of work/research has been already conducted for the generic VNF placement problem, network latency among various network components has not been comprehensively considered yet. To address this concern, in this article, we design a more comprehensive model based on real measurements to capture network latency among VNFs with more granularity to optimize placement of VNFs in CDCs. Experimental results are promising and indicate that our approach, namely VNF Low-Latency Placement (VNF-LLP), can reduce network latency by up to 64.24% (50.33% in average) compared with two generic algorithms. Furthermore, it has a lower lead time (time to find a suitable VM to host a VNF) as compared with two classic approaches.

Petroleum industry has started to embrace the advanced Petroleum Cyber-Physical System (CPS) technologies. Offshore petroleum CPS is particularly difficult to build, mainly due to the challenge in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically used for leak detection, enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and large computational overhead. In this work, a cross entropy optimization based leak detection technique is proposed to detect the oil leak, which also facilitates the understanding of the oil leak induced marine pollution. Experimental results on a real Penglai oil spill event demonstrate that the proposed technique can effectively identify the sources of oil spills with accuracy of up to 90.78%.

Sparse unmixing has been successfully applied to hyperspectral remote sensing imagery based on the assumption that the observed image signatures can be expressed in a linear sparse regression with a large standard spectral library. Prior work for sparse unmixing usually utilizes L1norm or Laplacian distribution to promote sparsity. Unfortunately, the L1norm is not differentiable, which may lead to unstable results. In this paper, we adopt Bregman divergence for sparse unmixing, which is a differentiable, smoother prior. Based on the Maximum A Posterior (MAP) estimation, the proposed method has achieved sparse, stable and precise fractional abundances. The experimental results both simulated dataset and the real hyperspectral image demonstrate the effectiveness of the proposed differentiable sparse unmixing algorithm.

Software systems for data-centric smart city applicationsThe smart city is the key technology for efficient management, sustainable development, and efficient governance in the current worldwide urbanization process.It incorporates the latest information technologies particularly data-driven trends to improve support for the everyday life of people, especially in security, transportation, and social services.The smart city paradigm can be specified as a large-scale distributed system in which the massive data generated by smart electronic devices, smart environments, and Internet of Things (IoT) can be organized, managed, and analyzed.The design of scalable software applications, frameworks, and packages is important in forming approaches which integrate smart city infrastructures (environment, physical ICT infrastructure), public processes, and services.Recently, software system development for data-centric smart city applications has become a very active area of research in academia and has attracted significant interest from industry.Since computing issues for smart cities are highly interdisciplinary and cover various topics, a special issue of Software: Practice and Experience provides the ideal forum for presenting and discussing the latest research.The goal of this special issue is to present outstanding research results in regard to software systems for data-centric smart city applications.We received 23 manuscript submissions in total; of these, 7 papers were accepted after several rounds of very constructive and deep reviews.Large-scale wireless communication is the fundamental infrastructure needed to ensure the operation of smart city, cloud computing, the IoT, etc.A relay network can provide an efficient solution to reliable transmission of large amounts of data.However, when base stations are densely populated, energy consumption becomes a critical problem.To solve this problem, Lam et al propose a software system for robust power management taking uncertain channel gains into consideration.The system relies on a distributed power allocation algorithm to reduce the overhead of extra information exchange while guaranteeing performance with respect to energy savings and robustness in a dynamic communications environment.Smart city uses many data sources.To manage the massive volume of data, data compression is essential, especially for surveillance videos.Since lossy compression methods cause information loss, a high compression ratio negatively affects data analysis.Xiao et al tackled this problem by proposing a sensitive-object-oriented compression method for surveillance videos.Regions with sensitive objects critical to the analysis of surveillance videos are detected prior to compression.Higher bit rates are assigned to these regions to enable a high compression ratio for the whole video stream without sacrificing the performance of data analysis centered on the sensitive objects.Data analysis is at the very core of smart city applications.Four interesting topics are included in this special issue, covering cloud-based mining for traffic data, low-cost computing for face tracking, fast processing of forensic data, and accurate analysis of human emotions: 1. Yu et al propose a SPARK-based software framework for trajectory pattern mining and trajectory clustering for taxi journeys.The proposed algorithms adopt in-memory computation and load the trajectory sequences into resilient distribution datasets to overcome high I/O overhead and communication overhead.High efficiency and scalability were achieved for the whole distributed framework.2. Fast processing of forensic data in heterogeneous distributed system is demonstrated by Quick et al.Their work gives digital forensic specialists a method and framework to review and analyze media in a timely manner through digital forensic data reduction.

Special issue on Big Data and Cloud of Things (CoT)Cloud computing and Internet of Things (IoT) are two technologies that are already becoming part of our daily lives and are attracting significant interest from both industry and academia.The Cloud of Things (CoT) is a vision inspired from the IoT paradigm where everyday devices, namely, 'smart objects', are fully connected to the internet and are integrated with the cloud.It is expected the IoT will grow to 35 billion units by 2020, making it one of the main sources of 'Big Data' with characteristics such as volume, heterogeneity, complexity, velocity, and value.In recent years, IoT has given rise to a number of new CoT paradigms (but not limited to) including: Sensing-as-a-Service, Sensing-and Actuation-as-a-Service, Video-Surveillance-as-a-Service, Big Data Analytics-asa-Service, Data-as-a-Service, Sensor-as-a-Service, and Sensor-Event-as-a-Service. Cloud computing is a more mature technology compared to IoT.It can offer virtually unrestricted capabilities (e.g., storage and computation) to support IoT services and application that can exploit the data produced from IoT devices.The cloud essentially acts as a transparent layer between the IoT and applications providing flexibility, scalability, and hiding the complexities between the two layers (IoT and applications).However, the integration of cloud and IoT into Cloud of Things is not straightforward and imposes several challenges.These challenges include IoT device and service discovery, IoT device integration, big data management and analytics, cloud monitoring and orchestration for distributed IoT applications, mobility issues in cloud access, privacy and security, and SLA management for both cloud and IoT.Specific attention must be paid to address a range of issues from IoT data collection, storage, processing, analytics on demand to automatic provision and management of cloud resources to support the growing population of things.Hence, this special issue solicits paper related to topics including CoT architectures and models for smart provision of CoT applications, data management challenges facing CoT applications, software and tools to monitor, manage, deploy and deliver CoT applications, quality of service and related SLA management and policies for CoT applications, and security and privacy challenges facing CoT applications.The call for special issues received a number of submissions.After a two-phase peer review process, we have accepted 10 high-quality papers related to the aforementioned areas of interest.The first paper titled Using adaptive resource allocation to implement an elastic MapReduce framework by Jiaqi Zhao, Changlong Xue, Xinlin Tao, Shugong Zhang, and Jie Tao addresses the runtime resource demand challenge faced by application running on MapReduce frameworks.The proposed approach is capable of making the map reduce application, aware of overloading or under-loading situations with the resources allocated.They have extended the existing Hadoop MapReduce resource manager to implement the proposed strategy and validated the concept on an high-performance computing cluster with standard benchmark applications.Experimental results show a significant performance gain, for example, an up to 45% improvement in execution time for running multiple applications.The second paper titled A traffic hotline discovery method over cloud of things using big taxi GPS data by Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Chunhua Hu, and Jinjun Chen addresses the challenge of discovering traffic hotline in CoT environments.Traffic hotlines are identified as the traffic lines with intensive traffic flows among traffic spots.They propose a hotline discovery method over CoT by establishing a hotline discovery principle.They have implemented their approach on SAP HANA cloud and tested it using big taxi global positioning system data under two application scenarios.

In a petroleum cyber-physical system (CPS), interwell connectivity estimation is critical for improving petroleum production. An accurately estimated connectivity topology facilitates reduction in the production cost and improvement in the waterflood management. This paper presents the first study focused on computer-aided design for a petroleum CPS. A new CPS framework is developed to estimate the petroleum well connectivities. Such a framework explores an innovative water/oil index integrated with the advanced cross-entropy optimization. It is applied to a real industrial petroleum field with massive petroleum CPS data. The experimental results demonstrate that our automated estimations well match the expensive tracer-based true observations. This demonstrates that our framework is highly promising.

Ensuring the security and privacy of vehicular ad hoc networks (VANETs) and related services such as secure payment has been the focus of recent research efforts. Existing secure payment solutions generally require stable and reliable network connection. This is, however, a challenge in a VANET setting. Drive-thru Internet, a secure payment solution for VANETs, involves a great number of fast-moving vehicles competing for connections/communications simultaneously. Thus, service providers may find it challenging to provide real-time payment services or may have to sacrifice the confidentiality and the authenticity of payment vouchers for usability. In this article, we propose a secure installment payment framework for drive-thru Internet deployment in a VANET setting. The framework also provides the capability to embody properties such as confidentiality of payment vouchers, offline signature verification, periodical reconciliation, and installment payment. Performance evaluation and security analysis demonstrate the utility of the framework in a VANET setting.

The fusion of remote sensing images with different spatial and temporal resolutions is needed for diverse Earth observation applications. A small number of spatiotemporal fusion methods that use sparse representation appear to be more promising than weighted- and unmixing-based methods in reflecting abruptly changing terrestrial content. However, none of the existing dictionary-based fusion methods consider the downsampling process explicitly, which is the degradation and sparse observation from high-resolution images to the corresponding low-resolution images. In this paper, the downsampling process is described explicitly under the framework of compressed sensing for reconstruction. With the coupled dictionary to constrain the similarity of sparse coefficients, a new dictionary-based spatiotemporal fusion method is built and named compressed sensing for spatiotemporal fusion, for the spatiotemporal fusion of remote sensing images. To deal with images with a high-resolution difference, typically Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS), the proposed model is performed twice to shorten the gap between the small block size and the large resolution rate. In the experimental procedure, the near-infrared, red, and green bands of Landsat-7 and MODIS are fused with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than five state-of-the-art methods, which prove the feasibility of incorporating the downsampling process in the spatiotemporal model under the framework of compressed sensing.

It has long been an important issue in various disciplines to examine massive multidimensional data superimposed by a high level of noises and interferences by extracting the embedded multi-way factors. With the quick increases of data scales and dimensions in the big data era, research challenges arise in order to (1) reflect the dynamics of large tensors while introducing no significant distortions in the factorization procedure and (2) handle influences of the noises in sophisticated applications. A hierarchical parallel processing framework over a GPU cluster, namely H-PARAFAC, has been developed to enable scalable factorization of large tensors upon a “divide-and-conquer” theory for Parallel Factor Analysis (PARAFAC). The H-PARAFAC framework incorporates a coarse-grained model for coordinating the processing of sub-tensors and a fine-grained parallel model for computing each sub-tensor and fusing sub-factors. Experimental results indicate that (1) the proposed method breaks the limitation on the scale of multidimensional data to be factorized and dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., the runtime increases in the order of n2 when the data volume increases in the order of n3, (2) H-PARAFAC has potentials in refraining the influences of significant noises, and (3) H-PARAFAC is far superior to the conventional window-based counterparts in preserving the features of multiple modes of large tensors.

Recently, CSR, GFZ and JPL have launched GRACE RL05 data with advantages in the spatial resolution and precision. This study explores the capability of GRACE to detect the Terrestrial Water Storage (TWS) variations in Jing-Jin-Ji Region. In this paper, we make GRACE RL05 data spatial filtering, compute and analyze TWS variations time-series and spatial trend distribution. Results show TWS in Jing-Jin-Ji Region possess obvious decreasing trend, TWS variations of CSR, JPL and GFZ from 2004 to 2014 respectively reduce in the rate of -1.13337 cm/a, -1.43606 cm/a, -1.69749 cm/a. And TWS variations spatial distribution of CSR, GFZ, JPL are strongly consistent. Besides, results also show that TWS variations accelerate significantly and decrease in southeast faster than in northwest. It is effortless to acquire that the predominant reason of TWS variations is population growth leading to badly demand for water resources. And these results tell us that TWS decreasing should be concerned.

In this paper, the blind restoration of a degraded image with an auxiliary image from another sensor is considered. In a typical multispectral satellite imaging system, multiple images from different sensors of the same area are available. When one of those images in a multiple image set is degraded, another image in the set can be used as a prior image for restoration. A hybrid algorithm based on the sparse representation using an auxiliary image is proposed in this paper. In this approach, the cost function for regularization has two terms: regularization from the degraded image being restored and the regularization from the auxiliary image. The amount of prior information from the auxiliary image to be used in the hybrid algorithm is determined based on the similarity between the auxiliary image and the degraded image. The proposed algorithm is applied to both simulated and real multispectral images, and the performance of the proposed algorithm is compared with those of other image restoration algorithms. In both quantitative and qualitative comparisons, the proposed algorithm performed better than other algorithms.

Remote sensing image reconstruction from undersampled data is very much required by the onboard imaging system to cut down data volume and maintain image quality. Nonlocal low-rank regularization deriving from group sparsity, low rank, singular-value thresholding, and nonconvex surrogate functions have recently emerged for image recovery. To use nonlocal low-rank compressed sensing for remote sensing image reconstruction, spectral and temporal redundancy are considered in this letter by utilizing the similarity of correlated bands or historical records. Prior structural knowledge helps to group nonlocal similar blocks more accurately. Oversmoothness of low-rank regularization is improved by injecting referenced structures selectively. The proposed compressed sensing method is tested on satellite images from MODIS, LandSat-7, LandSat-8, IKONOS, and Google Earth to make clear that it outweighs state-of-the-art methods in maintaining fidelity and high visual details.

The papers in this special section focus on next generation urbanization that incorporates smart city development. The development of smart cities is viewed as the key to the next generation urbanization process for improving the efficiency, reliability, and security of a traditional city. The concept of smart city includes various aspects such as environmental sustainability, social sustainability, regional competitiveness,natural resources management, cybersecurity, and quality of life improvement. With the massive deployment of networked smart devices/sensors, an unprecedentedly large amount of sensory data can be collected and processed by advanced computing paradigms, which are the enabling techniques for smart city. For example, given historical environmental, population, and economic information, salient modeling and analytics are needed to simulate the impact of potential city planning strategies, which will be critical for intelligent decision-making.

As we come to terms with various big data challenges, one vital issue remains largely untouched. That is service level agreement (SLA) management to deliver strong Quality of Service (QoS) guarantees for big data analytics applications (BDAA) sharing the same underlying infrastructure, for example, a public cloud platform. Although SLA and QoS are not new concepts as they originated much before the cloud computing and big data era, its importance is amplified and complexity is aggravated by the emergence of time-sensitive BDAAs such as social network-based stock recommendation and environmental monitoring. These applications require strong QoS guarantees and dependability from the underlying cloud computing platform to accommodate real-time responses while handling ever-increasing complexities and uncertainties. Hence, the over-reaching goal of this PhD research is to develop novel simulation, modelling and benchmarking tools and techniques that can aid researchers and practitioners in studying the impact of uncertainties (contention, failures, anomalies, etc.) on the final SLA and QoS of a cloud-hosted BDAA.

It has long been an important issue in various disciplines to examine massive multi-dimensional data by extracting the embedded multi-way factors. With the quick increases in both scales and dimensions of data under analysis, research challenges arise in order to reflect the dynamics of large-scale tensors while introducing no significant distortions in the factorization procedure in sophisticated applications. A massively parallel computing framework, namely H-PARAFAC, has been developed to enable Parallel Factor Analysis (PARAFAC) of massive tensors upon a "divide-and-conquer" theory (a modified alternating least squares approach). The hierarchical framework incorporates a coarse-grained model for coordinating the processing of sub tensors and a fine-grained parallel model for computing each sub tensor and fusing sub-factors. Experiments have been performed on a GPU cluster, and the results indicate that (1) the proposed method breaks the limitation on the size of data to be factorized, and (2) it dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., The runtime increases linearly with the data volume increases in the order of n3.

Summary In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as Makespan , is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem with Makespan and energy consumption as the main criteria. We apply the dynamic voltage and frequency scaling model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.

Summary With the incessant expansion of applications and the frequent update of the software, Science Gateway for Massive Remote Sensing Image Processing (SGMRSIP), developed by client/server model or traditional browser/server model, has received more and more challenges. Fortunately, the Web 2.0 technologies, proposed in recent years, bring us a new user experience (UE) that has a fast response speed and a good interface. In particular, the remote sensing image can be processed smoothly in the absence of client software by Web 2.0 technologies. Hence, a Web 2.0‐based browser/server model is designed for SGMRSIP to enhance the UE in this paper. Firstly, functions of a parallel remote sensing image processing portal, based on high performance cluster and client/server model, are summarized. And then, a Web 2.0‐based interaction model is built, and all these functions are accomplished again on the basis of this model. Finally, the Web 2.0‐based Science Gateway is achieved. In addition, we design different workflows for different satellite data, and all the processing tasks are finished successfully to verify the feasibility of this Science Gateway. The experimental results showed that the software scalability and interaction were improved and a better UE was achieved, compared with the existing SGMRSIP. Copyright © 2013 John Wiley & Sons, Ltd.

Basic compressed-sensing algorithms for image reconstructions mainly deal with the computation of sparse regularization. Remote sensing applications often have multisource or multitemporal images whose different components are acquired separately. Therefore, this letter considers the reconstruction of a remote sensing image using an auxiliary image from another sensor or another time as the reference. For this application, a new compressed-sensing object function is developed that uses a reference image as a prior. In the new model, the sparsity constraints in the transform domain come from the target image, and the gradient priors in the spatial domain come from the auxiliary reference image. The hybrid regularization is optimized by basing the algorithm on the Bregman split method. The proposed method shows better performances when compared with other three popular compressed-sensing algorithms.

Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices. There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency. Moreover, the approach must be able to quickly analyze the neural data of exponentially growing scales and sizes, in tens or even hundreds of channels, so that timely conclusions and decisions may be made. A salient approach to multi-way data analysis is the parallel factor analysis (PARAFAC) that manifests its effectiveness in the decomposition of the electroencephalography (EEG). However, the conventional PARAFAC is only suited for offline data analysis due to the high complexity, which computes to be$O(n^{2})$with the increasing data size. In this study, a large-scale PARAFAC method has been developed, which is supported by general-purpose computing on the graphics processing unit (GPGPU). Comparing to the PARAFAC running on conventional CPU-based platform, the new approach dramatically excels by${>}360$times in run-time performance, and effectively scales by${>}400$times in all dimensions. Moreover, the proposed approach forms the basis of a model for the analysis of electrocochleography (ECoG) recordings obtained from epilepsy patients, which proves to be effective in the epilepsy state detection. The time evolutions of the proposed model are well correlated with the clinical observations. Moreover, the frequency signature is stable and high in the ictal phase. Furthermore, the spatial signature explicitly identifies the propagation of neural activities among various brain regions. The model supports real-time analysis of ECoG in${>}1{,}000$channels on an inexpensive and available cyber-infrastructure.

Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users' direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.

With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and virtual machine (VM ) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multi-criteria-based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius’ applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA.

In this work an ultra-scalable algorithm is designed and optimized to accelerate a 3D compressible Euler atmospheric model on the CPU-MIC hybrid system of Tianhe-2. We first reformulate the mesocale model to avoid long-latency operations, and then employ carefully designed inter-node and intra-node domain decomposition algorithms to achieve balance utilization of different computing units. Proper communication-computation overlap and concurrent data transfer methods are utilized to reduce the cost of data movement at scale. A variety of optimization techniques on both the CPU side and the accelerator side are exploited to enhance the in-socket performance. The proposed hybrid algorithm successfully scales to 6,144 Tianhe-2 nodes with a nearly ideal weak scaling efficiency, and achieve over 8 percent of the peak performance in double precision. This ultra-scalable hybrid algorithm may be of interest to the community to accelerating atmospheric models on increasingly dominated heterogeneous supercomputers.

Cloud computing assembles large networks of virtualised ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications. Big Data applications have become a common phenomenon in domain of science, engineering, and commerce. Large-scale, heterogeneous, and uncertain Big Data applications are becoming increasingly common, yet current cloud resource provisioning methods do not scale well and nor do they perform well under highly unpredictable conditions (data volume, data variety, data arrival rate, etc.). Much research effort have been paid in the fundamental understanding, technologies, and concepts related to autonomic provisioning of cloud resources for Big Data applications, to make cloud-hosted Big Data applications operate more efficiently, with reduced financial and environmental costs, reduced under-utilisation of resources, and better performance at times of unpredictable workload. Targeting the aforementioned research challenges, this special issue compiles recent advances in Autonomic Provisioning of Big Data Applications on Clouds. The special issue articles are briefly summarized.

Simulation study on evacuation scenarios has gained tremendous attention in recent years. Two major research challenges remain along this direction: (1) how to portray the effect of individuals' adaptive behaviors under various situations in the evacuation procedures and (2) how to simulate complex evacuation scenarios involving huge crowds at the individual level due to the ultrahigh complexity of these scenarios. In this study, a simulation framework for general evacuation scenarios has been developed. Each individual in the scenario is modeled as an adaptable and autonomous agent driven by a weight-based decision-making mechanism. The simulation is intended to characterize the individuals' adaptable behaviors, the interactions among individuals, among small groups of individuals, and between the individuals and the environment. To handle the second challenge, this study adopts GPGPU to sustain massively parallel modeling and simulation of an evacuation scenario. An efficient scheme has been proposed to minimize the overhead to access the global system state of the simulation process maintained by the GPU platform. The simulation results indicate that the “adaptability” in individual behaviors has a significant influence on the evacuation procedure. The experimental results also exhibit the proposed approach's capability to sustain complex scenarios involving a huge crowd consisting of tens of thousands of individuals.

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

Remote sensing applications in Digital Earth are overwhelmed with vast quantities of remote sensing (RS) image data. The intolerable I/O burden introduced by the massive amounts of RS data and the irregular RS data access patterns has made the traditional cluster based parallel I/O systems no longer applicable. We propose a RS data object-based parallel file system for remote sensing applications and implement it with the OrangeFS file system. It provides application-aware data layout policies, together with RS data object based data I/O interfaces, for efficient support of various data access patterns of RS applications from the server side. With the prior knowledge of the desired RS data access patterns, HPGFS could offer relevant space-filling curves to organize the sliced 3-D data bricks and distribute them over I/O servers. In this way, data layouts consistent with expected data access patterns could be created to explore data locality and achieve performance improvement. Moreover, the multi-band RS data with complex structured geographical metadata could be accessed and managed as a single data object. Through experiments on remote sensing applications with different access patterns, we have achieved performance improvement of about 30 percent for I/O and 20 percent overall.

In this paper, we propose a new compressed sensing based approach to enhance the spatial-temporal resolution of the remote sensing images with a pair of time-continuous spatial-temporal images and a low spatial resolution image at the same place. In compressed sensing, the measurement matrix is a key element to success. This paper presents a novel solution space model for designing the measurement matrix by establishing the correspondence between the spatial-temporal image pair to enhance the spatial-temporal resolution. The matrix we get does not only reflect the relationship between the high- and the low-spatial resolution images, but also have high randomness, thus satisfies the reconstruction requirements (e.g., RIP restriction) in compressed sensing. To verify the effectiveness of our method, we give the experimental reconstructed results and compare our results with the traditional Gaussian Random matrix and the Toplitz matrix. The experiment demonstrates the effectiveness and superiority of the proposed method.

In remote sensing applications, there are often multi-source or multi-temporal images whose different components are acquired separately. Therefore, a part of the acquired images in multi-component data can be used as priors. In this paper, the reconstruction of a remote sensing image using an auxiliary image from another sensor or another time as the reference is considered. For this application, a new compressed sensing object function with an reference image as a prior is developed. In the new model, the sparsity constraints in transform domain comes from the target image, and the gradient priors in spatial domain comes from auxiliary reference image. To optimizing the the hybrid regularization, the algorithm is based on Bregman split method. The performance of the algorithm is evaluated both qualitatively and quantitatively. The results of experiment confirm that the proposed algorithm gets higher peak signal to noise ratio (PSNR) than other approaches without reference images as priors.

Adaptive sparse representations of signals have drawn considerable interest in the past decade. In this paper, we address the problem of training dictionaries for massive images and propose a new algorithm for adapting dictionaries by extending the classical K-SVD based on only a single image. The approach presented in this paper aims at training the adapting dictionary from massive samples, other dictionary learning methods such as Online Dictionary Learning (ODL) and Recursive Least Squares Dictionary Learning Algorithm (RLS-DLA) also could train the dictionary by using relative large samples. Our method is competed with the above two state-of-the-art dictionary learning methods. Experiments demonstrate the effectiveness of the proposed dictionary learning in dealing with massive spatial-temporal remote sensing.

In remote sensing applications, we often encounter data loss issue. When calculating NDVI of some regions, remote sensing data set from Landsat should be used. However, the data set is maybe incomplete. Our empirical method to deal with this problem is to use data set from HJ-1 instead. Naturally, we surmise that the data set obtained by HJ-1 must correlate with the data set obtained by Landsat on condition that the data sets from two satellites have been registered. In this work, We first learned a dictionary and defined a metric under this dictionary to measure NDVI of data set in sparse domain. Through the experiments we can draw the conclusion that NDVI also can be calculated in sparse domain by our method and there is a high correlation between NDVI and NDVI calculated in sparse domains denoted as S NDVI. Based on this, we use the difference of S NDVIs of two data sets to measure the correlation of them in sparse domain which has a linear relationship with the correlation of NDVIs of two data sets.

The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.

SUMMARY This article is dedicated to techniques and theories of image fusion in automatic ways and addresses two issues—the parameter setting and quality assessment. Optimal parameters are in demand for specific applications or comparison between fusion methods because, as basic evidence, different parameters bring different fusion effects varying over a large range. In this paper, we propose a general framework of online parameter training to search optimal values that best suit input images. Furthermore, we optimized the compute‐intensive training process using parallelization and genetic algorithm, as well as patches extraction. We also propose a metric—spatial and spectral distortion—as the learning target. The spatial and spectral distortion is a fuzzy combination of mean potential energy measuring spatial distortion and Q4 measuring spectral distortion. Optimization validation on weighted Gram–Schmidt fusion indicated linear or superlinear acceleration ability, which proved that the proposed learning framework can speed up the learning process of image fusion to an acceptable time, and can thus be applied to high‐performance platforms to process large volumes of data. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY Data intensive applications of remote sensing data processing are more and more widespread resulting from the evolutions in computer and network technologies. Especially, bags‐of‐tasks (BoTs) applications with a mass of sharing input files and directed acyclic graph (DAG) applications with data dependencies in a widely distributed computing environment bring new challenges. In this article, a strategy of partitioning group based on hypergraph (PGH) is introduced to formulate the model of sharing files. Within the PGH algorithm, BoTs applications would be partitioned into several groups to minimize the time of data transferring. We also adopted another scheduling policy, which is called optimized task tree (OTT) strategy to handle the DAG workflow of massive remote sensing data processing with data dependencies. A scheduling queue of DAG tasks would be updated according to the priorities changing. With the help of GridSim simulation environment, we designed the Gridlets within scheduler to test the performance of PGH and OTT. Copyright © 2013 John Wiley & Sons, Ltd.

Remote sensed imagery mosaicking at large scale has been receiving increasing attentions in regional to global research. However, when scaling to large areas, image mosaicking becomes extremely challenging for the dependency relationships among a large collection of tasks which give rise to ordering constraint, the demand of significant processing capabilities and also the difficulties inherent in organizing these enormous tasks and RS image data. We propose a task-tree based mosaicking for remote sensed imageries at large scale with dynamic DAG scheduling. It expresses large scale mosaicking as a data-driven task tree with minimal height. And also a critical path based dynamical DAG scheduling solution with status queue named CPDS-SQ is provided to offer an optimized schedule on multi-core cluster with minimal completion time. All the individual dependent tasks are run by a core parallel mosaicking program implemented with MPI to perform mosaicking on different pairs of images. Eventually, an effective but easier approach is offered to improve the large-scale processing capability by decoupling the dependence relationships among tasks from the complex parallel processing procedure. Through experiments on large-scale mosaicking, we confirmed that our approach were efficient and scalable.

Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware scheduling has become a major research issue. In this work we present two implementations of classical genetic-based data-aware schedulers of independent tasks submitted to the grid environment. The results of a simple. empirical analysis confirm the high effectiveness of the genetic algorithms in solving very complex data intensive combinatorial optimization problems.

Ecohydrological modeling is essential to assess impact of climate change and intense human activities (land use change) on hydrological process and ecosystem to support watershed management. The traditional ecohydrological models have the deficit in coupling ecological and hydrological processes, and parameterizing vegetation parameters. Recently, optimality hypothesis, proposed by Eagleson, has been introduced to ecohydrology research which has given rise to a novel framework for modeling ecohydrological process. However, as optimality-based model has just spring up in ecohydrology, it has not been fully tested and more application of this kind of model is needed. In this study, we tried to apply an optimality-model to simulate ecohydrological process so as to test the model and support watershed management. The model has been tested in the Walnut Gulch watershed. With collected data from the study area, the model was used to simulate hourly evaportranspiration and GPP and so on. The validation result showed that, the results produced by the model were in good agreement with observed values. The VOM model can effectively overcomes the problem of traditional watershed ecohydrological models in depict ecological and hydrological coupling, the haunting task of vegetation parameters calibration. This could come to a conclusion the optimality-based ecohydrological model could be a potential approach to simulate ecohydrological process.

Marine oil spills is one of the most serious sea pollution which has a horrible effect on environment, economy, and quality of life for coastal inhabitants. How to reduce the risk of oil spill disasters has become one of the principal problems faced with marine environment management. Oil spill observation and spill processes simulation are two main parts for oil spill accident controlling and management. Traditionally, the oil spill information detection and spill simulation is disjoined without any feedback. The modeling approach is all conducted with fixed structure and static data input while the observation system is always static with fixed monitoring scheme. In such a circumstance, neither the observation system nor the simulation can provide highly accurate information. This paper propose a new framework combining oil spill monitoring and simulation as a symbiotic feedback control system based on the theory of Dynamic Data Drive Application System (DDDAS), a new paradigm dynamically integrated simulations, measurements, and applications. The numerical oil spill model can accepts real time data from remote sensing monitoring which assure modeling a more accurate and more reliable outcomes. Multiple simulations will be executed with different remote sensing monitoring scheme and the feedback from simulation guide and determine how to gather the data. For mathematical modeling of the DDDAS based marine oil spill management system, we built a multi-stage optimization model. Such system could promise more accurate prediction and more reliable outcomes with real time oil spill input, which will improve modeling technologies, advance prediction capabilities of simulation systems, and enhance oil spill monitoring.

SUMMARY Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest‐end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch‐based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN‐customized topologies and customized addressing protocols in the large‐scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Earth observation applications are now facing the challenges of managing and processing massive data sets from multiple sources from large‐scale distributed data centers (DCs). To solve this research problem, this paper presents an infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images. The proposed system is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager. Access security and information service are introduced to support this architecture of MDC. We collaboratively organized the algorithm, and data belonged to the MDC in the manner of workflow. In practice, we succeeded in working out the concrete problems regarding procedures in processing applications collaboratively and transfer the massive remote sensing dataset fast and with stable cross‐MDC. On the basis of the previously mentioned research work, we will investigate the platform integration of MDC. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small‐sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust‐based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud monitoring involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized services (e.g., CPU, storage, network, appliances, etc.), the physical resources they share, and the applications running on them or data hosted on them. Monitoring techniques and services can help a cloud provider or application developer in regards to: (i) keeping the cloud services and hosted applications operating at peak efficiency; (ii) detecting variations in service and application performance; (iii) accounting the SLA violations of certain QoS parameters; and (iv) tracking the leave and join operations of cloud services due to failures and other dynamic configuration changes. In this paper, we describe the PhD research motivation, question, and approach and methodology related to developing novel cloud monitoring techniques and services enabling automated application QoS management under uncertainties.

Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing amount of algorithms of higher complexity. Parallel programming for data-intensive applications like massive remote sensing image processing on parallel systems is bound to be especially trivial and challenging. We propose a C++ template mechanism enabled generic parallel programming skeleton for these remote sensing applications in high performance clusters. It provides both programming templates for distributed RS data and generic parallel skeletons for RS algorithms. Through one-side communication primitives provided by MPI, the distributed RS data template could provide a global view of the big RS data whose sliced data blocks are scattered among the distributed memory of cluster nodes. Moreover, by data serialization and RMA (Remote Memory Access), the data templates could also offer a simple and effective way to distribute and communicate massive remote sensing data with complex data structures. Furthermore, the generic parallel skeletons implement the recurring patterns of computation, performance optimization and pass the user-defined sequential functions as parameters of templates for type genericity. With the implemented skeletons, Developers without extensive parallel computing technologies can implement efficient parallel remote sensing programs without concerning for parallel computing details. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient.

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

In a Delay Tolerant Network (DTN), the nodes have intermittent connectivity and complete path(s) between the source and destination may not exist. The communication takes place opportunistically when any two nodes enter the effective range. One of the major challenges in DTNs is message forwarding when a sender must select a best neighbor that has the highest probability of forwarding the message to the actual destination. However, finding an appropriate route remains an NP-hard problem. This paper presents a concept of Checkpoint (CP) based message forwarding in DTNs. The CPs are autonomous high-end wireless devices with large buffer storage and are responsible for temporarily storing the messages to be forwarded. The CPs are deployed at various places within the city parameter that are covered by bus routes and where human meeting frequencies are higher. For the simulative analysis a synthetic human mobility model in ONE simulator is constructed for the city of Fargo, ND, USA. The model is tested over various DTN routing protocols and the results indicate that using CP overlay over the existing DTN architecture significantly decreases message delivery time as well as buffer usage.

Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. The neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also data intensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. The Map Reduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with Map Reduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyber infrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing.

HPC analytics has become increasingly vital to analyze the large volumes of data produced by sophisticated computing instruments. Meanwhile, with the successful development of cloud computing, more and more scientists are devoted to deploy HPC analytics in the ever-popular clouds, which poses new challenges mainly caused by different storage architectures, resource management mechanisms and programming APIs. Firstly, there exists a ``data semantics" gap between the way data are stored by Cloud platform and the way data will be accessed by the HPC Analytics. Secondly, data are mostly distributed across data nodes for in-house data-intensive clusters to achieve co-located computation and storage, however, it is challenging for the public clouds to mimic because their data are stored centrally. In this paper, we develop a new HPC analytics framework called NOHAA, to provide 1) a semantics-aware intelligent data upload interface and 2) a locality-aware hierarchical storage system in support of co-located computation and storage on Windows Azure. Our extensive real world experiments show that NOHAA significantly reduces the average data access time by up to 85% and accelerates the HPC analytics execution time by a factor of 2 to 7.

Cloud computing has promoted the widespread use of virtualized machines. A question arises: How does virtualization influence the performance of running applications? The answer must be a common interest of application developers and users. This paper describes the results of our performance evaluation on a virtualized multicore machine. We tested a set of benchmark applications and detected some general features that should be considered when running applications on a virtualized multicore machine. We also studied the application execution behavior using profiling tools. We found the reason for unexpectedly poor performance of an OpenMP application in a virtualized setting and optimized the program. The optimization resulted in a significant performance gain.

To reduce the energy consumption and build a sustainable computer infrastructure now becomes a major goal of the high performance community. A number of research projects have been carried out in the field of energy-aware high performance computing. This paper is devoted to categorize energy-aware computing methods for the high-end computing infrastructures, such as servers, clusters, data centers, and Grids/Clouds. Based on a taxonomy of methods and system scales, this paper reviews the current status of energy-aware HPC research and summarizes open questions and research directions of software architecture for future energy-aware HPC studies.

Grid computing uses a job submission model that requires the users to perform a set of interactive operations for executing an application on the Grid. Grid users are therefore burdened with the tasks of understanding the basic concept of Grid computing and the details of job management. Cloud computing, on the other hand, applies a utility model that allows the user to access the underlying platform via Web services. This work brings the Cloud concept to the Grid with a result of replacing the job submission model with a service infrastructure. In this case, Grid applications are presented as Web services that can be executed on the Grid automatically without user interactions. All issues related to job management are performed by the system. The service infrastructure significantly simplifies the users' task in accessing the Grid.

Spatial data processing in general involves large-scale distributed computing resources across multiple data centers and distributed massive data sets. Task scheduling of spatial data processing across distributed data centers brings new research issues and calls for novel scheduling mechanisms and frameworks. This paper analyzes the features of spatial data processing, identifies the research issues of task scheduling in massive spatial data processing, and proposes task model & scheduling mechanisms for task scheduling in spatial data processing.

Introduction to a special issue of Concurrency and Computation: Practice and Experience containing selected papers from the Fourth International Workshop on Workflow Management (ICWM2009), which was held on 04 May 2009 in Geneva, Switzerland.

Abstract Electronic Health Records (EHRs) have many potential advantages over traditional paper records, such as wide scale access, error checking, and protection from physical damage to a record. As with any medical record, paper or electronic, both the patient's privacy and the document's integrity must be guaranteed. With initiatives such as Integrating the Healthcare Enterprise (IHE), computerized healthcare systems are able to share EHRs on a large scale, while protecting the patient's privacy rights. However, IHE does not yet meet the needs of all healthcare systems, as we will show with the eMOLST project. The eMOLST project delivers software in support of Medical Order for Life Sustaining Treatment (MOLST) forms and uses IHE specifications for cross enterprise document storage and sharing, patient identification, and user authentication and authorization. The Web‐based system provides secure access to electronic MOLST documents regardless of the patient's or healthcare provider's location. The eMOLST project allows a user to have Single Sign On (SSO) access to the system from either the user's associated enterprise, or through a Web portal shared amongst all users across all enterprises. In this paper, we show a security solution to allow SSO from multiple access points for IHE compliant systems. Copyright © 2011 John Wiley & Sons, Ltd.

Virtual machines offer unique advantages to the scientific computing community, such as Quality of Service(QoS) guarantee, performance isolation, easy resource management, and the on-demand deployment of computing environments. Using virtual machines as a computing resource within a distributed environment, such as Service Oriented Architecture (SOA), creates a variety of new issues and challenges that must be overcome. Traditionally, parallel task scheduling algorithms only focus on handling CPU resources. Using of a virtual machine, however, requires the monitoring and management of additional resource properties. Additionally, CPU, memory, storage, and software licenses must also be considered within the scheduling algorithm. The objective of this paper is to address these challenges of a multi-dimensional scheduling algorithm for virtual machines within a SOA. To do this, we deploy a testbed SOA environment composed of virtual machines which are capable of being registered, indexed, allocated, accessed, and controlled by our new parallel task scheduling algorithm.

Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

Similar Link Network (SiLN) is a semantic over layer on Web resources with similar relations instead of hyperlinks, which aims at providing plentiful semantics for intelligent Web activities. However, SiLN is difficult to be built based on cosine computation in large-scale Web resources due to its high building time complexity and weak connectivity. Herein, three strategies are proposed to address those issues. First, dividing and conquering strategy is applied to divide the large-scale Web resources into amounts of rough similar communities, which reduces SiLN's building time complexity significantly. After that, a multi-level structure network is designed to effectively manage the large-scale Web resources to guarantee SiLN's connectivity. Finally, two-level feedback with isolated resources strategy is developed to improve the accuracy of the building of SiLN. Experimental results have proved that our proposed method of building SiLN is feasible and efficient, with the merits of low complexity, good connectivity and high precision.

It has been widely known that various benefits can be achieved by reducing energy consumption for high end computing. This paper aims to develop power aware scheduling heuristics for parallel tasks in a cluster with the DVFS technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. This paper develops a power aware task clustering algorithm for parallel task scheduling Simulation results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

The Software as a Service (SaaS) methodology is a key paradigm of Cloud computing. In this paper, we focus on an interesting topic - to implement a Cloud computing functionality, the SaaS model, on existing production Grid infrastructures. In general, production Grids employ a Job-Submission-Execution (JSE) model with rigid access interfaces. In this paper we develop the Cyberaide onServe, a lightweight middleware with a virtual appliance. The Cyberaide onServe implements the SaaS methodology on production Grids by translating the SaaS model to the JSE model. The Cyberaide onServe virtual appliance is deployed on demand, hosts applications as Web services, accepts Web service invocations, and finally the Cyberaide onServe executes them on production Grids. We have deployed the Cyberaide onServe on the TeraGrid infrastructure and test results show Cyberaide onServe can provide the SaaS functionality with good performance.

Distributed virtual machines can help to build scalable, manageable, and efficient grid infrastructures. The work proposed in this paper focuses on employing virtual machines for grid computing. In order to efficiently run grid applications, virtual machine resource information should be provided. This paper first discusses the system architecture of virtual machine pools and the process of information retrieval from virtual machines. Based on the characterization of the system model, this paper presents the work on how to retrieve resource information from Xen/VMware virtual machines via VMware Common Information Model Software Development Kit and lightweight Java agents. The resource information is integrated into a grid information service. The work is implemented in a test bed with Xen/VMware virtual machines and the Globus Toolkit. With a performance evaluation and discussion on a real test bed, it is declared that the design and implementation of information services for virtual-machine-based grid systems are feasible, efficient, and scalable.

In recent years the power of Grid computing has grown exponentially through the development of advanced middleware systems. While usage has increased, the penetration of Grid computing in the scientific community has been less than expected by some. This is due to a steep learning curve and high entry barrier that limit the use of Grid computing and advanced cyberinfrastructure. In order for the scientists to focus on actual scientific tasks, specialized tools and services need to be developed to ease the integration of complex middleware. Our solution is Cyberaide Shell, an advanced but simple to use systemshell which provides access to the powerful cyberinfrastructure available today. Cyberaide Shell provides a dynamic interface that allows access to complex cyberinfrastructure in an easy and intuitive fashion on an ad-hoc basis. This is accomplished by abstracting the complexities of resource, task, and application management through a scriptable command line interface. Through a service integration mechanism, the shellpsilas functionality is exposed to a wide variety of frameworks and programming languages. Cyberaide Shell includes specialized experiment management and workflow commands that, with the scriptable nature of a shell, provide a set of services which where previously unavailable. The usability of Cyberaide Shell is demonstrated using a Water Threat Management application deployed on the TeraGrid.

With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.

Data centers now play an important role in modern IT infrastructures. Related research has shown that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures with in a data center will lead to higher hardware failure rates and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware resource management for data centers. This paper proposes an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then a thermal aware task scheduling algorithm with backfilling is presented which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing endurable decline in performance.

Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed computing dynamic environments for end-users. This paper reviews recent advances of Cloud computing, identifies the concepts and characters of scientific Clouds, and finally presents an example of scientific Cloud for data centers

Abstract Grid computing has now become the de facto standard for distributed computing and provides huge computing resources for high‐performance scientific and engineering applications. Recent advantages of virtual computing technologies make it possible to efficiently employ computing resources on‐demand. Distributed virtual machines (VMs) can help to build scalable, manageable and efficient Grid infrastructures. This paper is engaged to organize compact muon solenoid applications in virtual data system workflows and execute them on VMs in computational Grids. The work is implemented on VMware VMs. Test results are discussed and the performance is evaluated with comparison of related work. Copyright © 2008 John Wiley & Sons, Ltd.

In some visualization systems, the data and computational resources are distributed globally and users need to interact with these resources easily and efficiently. Real-time rendering for massive datasets is a computation intensive task. one solution is to distributes the rendering tasks over a set of computation units to achieve high rendering performance. This paper presents a recursive sort-first partitioning algorithm named Dynamic Pixel Bucket Partition (DPBP) for parallel rendering alone with their implementation and performance in a distributed rendering environment. This algorithm distributes rendering work loads evenly to individual rendering units to achieve fast, high quality rendering of massive data. Test results in a multi-cluster environment demonstrate the practicality of this rendering algorithm.

Scientific visualization, especially for massive data sets, has emerged in different disciplines recently. Generally, distributed scientific visualization applications require multiple resources, e.g., high-end computing resources to process data, high speed network for data transfer and large size database for data storage. Furthermore, these applications will meet research challenges, e.g., heterogeneous resources, geographically distributed environment and considerable communication delay. We study an application of distributed massive data rendering. We present infrastructure of the distributed rendering environment and explain how grid technologies are used in this application. Dynamic pixel bucket partition (DPBP) algorithm is a new algorithm proposed for task allocation of distributed rendering application in computational grids. Experiments in real test ted shows the performance of DPBP algorithm and the framework.