Flood segmentation using synthetic aperture radar (SAR) images is essential for determining the extent of inundation areas, and informing subsequent management recommendations. However, existing networks for flood segmentation using single modality SAR images often face inherent challenges, including interference from terrain shadows and water-like surfaces, leading to degraded segmentation performance. In this study, we introduced a multi-level multi-modal fusion network (FloodNet), in which an Adaptive Gated Feature Fusion Module (AGFFM) is designed to integrate multi-modal features from Sentinel-1 SAR images, Digital Elevation Model (DEM) and Joint Research Centre Global Surface Water (JRC-gsw). Furthermore, we proposed a semantic consistency constraint strategy to alleviate the blurring of water edges during the prediction process. Experiments on two publicly available flood datasets, C2S-Flood and ETCI-Flood, demonstrate the competitive performance of the proposed FloodNet compared with other state-of-the-art single- and multi-modal networks. The code is available at https://github.com/SuperPixelPioneer/Flood-Net.

Nighttime light (NTL) data holds irreplaceable value in research related to human activities, sustainable development, etc. However, the temporal and spatial continuity of high-resolution NTL data is challenging to meet the demands of large-scale applications. Spatiotemporal data fusion methods, by integrating low-resolution and high-resolution data, can fill in missing high-resolution data. Nevertheless, previous spatiotemporal data fusion research has primarily focused on multispectral data and face challenges when directly applied to NTL data. This research proposes a spatiotemporal fusion method specifically for NTL data, named the "LIght ON spatiotemporal data fusion" (LION) model. Experimental results indicate that LION demonstrated potential in predicting abrupt changes in NTL.

The Universal Thermal Climate Index (UTCI) is a crucial temperature index for describing human thermal comfort. With the continuous advancement of earth observation technologies, it has become feasible to monitor hourly global UTCI at kilometer-level resolution. However, the computational efficiency of UTCI calculations limits the production and application of UTCI, particularly time-series UTCI application at fine resolution. To address the abovementioned issue, this letter proposes a CUDA UTCI (CUTCI) method based on the graphics processing unit (GPU). CUTCI leverages the parallel computing capabilities of GPUs and kernel fusion techniques to improve parallelization and mitigate overhead during calculation. Experimental results demonstrated that, in comparison to operational UTCI and Thermofeel UTCI, CUTCI significantly improved computational efficiency by over 250 times and 17 times, respectively. Production of a single-period global UTCI at 0.1° resolution consumed less than 0.2 seconds. Experimental results revealed that CUTCI holds practical value in supporting real-time UTCI analysis and historical big data analysis over long time series.

Spatiotemporal data fusion provides an efficacious strategy for addressing data gaps within time series datasets. This approach significantly enhances the feasibility of large-scale remote sensing applications by, for example, enabling the creation of seamless Data Cubes (SDC). Nevertheless, strict data input requirements and low computational efficiency of current methods severely limit the practicality of large-scale SDC production. In this study, we propose an efficient spatiotemporal data fusion method, the Fast Variation-based Spatiotemporal Data Fusion (FastVSDF) method. FastVSDF consists of 3 steps, i.e., unmixing, distributing global residuals, and distributing local residuals. In the unmixing process, FastVSDF introduces the fast abundant variation classification (FAVC) to mitigate sample imbalance and expedite the unsupervised classification. Then, the in-class Gaussian weight function is introduced to accelerate the distribution of local residuals by considering the classification to introduce the information on spectral similarity. Besides, FastVSDF employs Fast Guided Filter to combat the "block artifacts" of global residuals efficiently. Results show that FastVSDF demonstrated superior performance over Fit-FC, STARFM, RASDF, and FSDAF. More importantly, FastVSDF yields a remarkable improvement in computational efficiency, reducing predicting time by 43 to 573 times. As a practical application, we generated the Sentinel-2 SDC for the Yangtze River Basin, China. The fusion process for a single period’s Yangtze River Basin dataset was accomplished within 20 minutes, with an average of 3.85 seconds for each Sentinel-2 scene. Comprehensively considering the efficiency, accuracy, feasibility, and universality, FastVSDF demonstrates the practical potential for constructing large-scale and long-term SDC. Our code will be publicly available at https://github.com/ChenXuAxel/FastVSDF.

Personnel and boat detection in Unmanned Aerial Vehicles (UAVs) imagery plays a crucial role in Open Water Search and Rescue Missions. The diverse perspectives and altitudes of UAV images often result in significant variations in the imagery’s appearance and dimensions of personnel and boats, and the false detections arising from water surface flares are acknowledged as a great challenge as well. Existing deep learning-based detection methods employ convolutional blocks with fixed kernel sizes to extract features from the imagery at a fixed spatial scale, which will lead to missed and false detections, and severely affect detection accuracy when there are substantial differences in the appearance and size of the target objects. In this paper, a spatial scale adaptive real-time object detection neural network, namely YoloOW, was proposed to tackle the challenge of personnel and boat detection amidst the diverse UAV imagery, which comprises a feature extractor, a feature enhancer, and a postprocessor. The OaohRep convolutional block was proposed as a pivotal component in constructing the YoloOW and applied to the feature extractor and the feature enhancer. Compared with general convolution blocks, the OaohRep convolution block can extract image features across a wide range of spatial scales, show better scale adaptability, and achieve faster detection speed due to its unique merged convolution layer design. OaohRepBi-PAN was proposed in the feature enhancer, which imitated the architecture of the classic algorithm SIFT and was successfully applied to deep learning models, showing better scale adaptability. A novel UAV detection box filter (UDBF) module was proposed in the postprocessor, which can effectively remove false detections caused by water surface flares. Experimental results demonstrate that our YoloOW model achieves 37.18% mAP on the SeaDronesSee dataset, surpassing the baseline by 8.43%. This notable improvement positions our model at the first of the leaderboard. The code will be available at https://github.com/Xjh-UCAS/YoloOW.

Tiled remote sensing data is essential for web-based remote sensing applications, widely applied in online map services, cloud-based remote sensing processing, etc. However, most existing tiling algorithms focus on tiling single remote sensing images with stand-alone machines. As the volume of remote sensing data increases, the demand for tiling high-resolution and large-scale remote sensing datasets increases dramatically. In this research, we propose a cloud-based parallel tiling algorithm for large-scale remote sensing datasets. A three-step processing flow is designed to implement the tiling of datasets composed of a set of images. Furthermore, three types of cloud-based storage are adopted to improve the efficiency of data extraction, namely, cloud storage, block storage, and NoSQL. We experimented with the proposed algorithm for tiling a national-scale 2 m resolution remote sensing dataset. The whole process took about 25.7 hours with up to 180 cores.

Today, remote sensing (RS) data are already regarded as “big data.” Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.

In this paper, management of multi-source heterogeneous disaster background data and fast reconstruction methods of 3D scene are proposed. 2D disasters background database system and 3D disaster visualization system are designed and implemented. By the shared metadata dimensions, integrated organization of disaster background data is realized. Based on multi-threaded pre-caching technology, fast scheduling and display of remote sensing images are achieved. Through interactive mechanism, synchronized data display and operation between 2D and 3D system are realized. Finally, 3D visualization of Wenchuan earthquake area and sea level flooded simulation in the New Coastal Region of Tianjin are took to verify the feasibility of the system. The system has characters of running fast, easy to operate, simple to deploy, and suitable for practical application in disaster rescue.

Global climate change has caused sea level rise, and one of the most extremely consequences are the increased frequency and hazards of the storm surge disasters, therefore, how to effectively assess the risk of storm surge disaster is of importance to hazard reduction and mitigation. However, the storm surge forecast models have complex parameters, which computational inefficiency. Traditional large-scale assessment usually takes elevation-area method based on GIS software, which result in large errors. The present study is attempted to: (1) Select proper two-dimensional hydraulic storm surge inundation model. This model not only has the physical realism but simple and efficient. (2)The present study will focus on the method to extract the required surface parameters based on the remote sensing data to integrate remote sensing data into the model. This project aims to provide the theoretical basis and methodologies for flood risk assessment in coastal zone of China.

This paper describes a 3D visualization computing methodology to aid the design of tremendous civil engineering in exploiting DEM and RS images. The methodology has been applied to the design and construction of astronomical telescope project, the FAST. Attempting to meet scientific and technological challenges in the process of the project, this paper has developed an innovational 3D computing platform. To simulate the virtual environment of the spot, 3D terrain model has been created based on QuickBird images and DEM, meanwhile, subtle model of massive main active reflector is loaded on the terrain. The algorithm of optimizing telescope antenna location in depressions is presented. In the virtual environment, considering the slope of the terrain, the distribution of feed supporting towers on the telescope has been optimized. It has been proved that the proposed methodology is of high efficiency in the practice of the application.

Urban heat island means the urban area has a higher temperature than peripheral regions, especially in summer. The result that urban temperature is higher than rural area is because the special urban underlining surface, human heat resources and local atmosphere circumfluence. Study indicates that urban land surface temperature and urban vegetation fraction are two important factors in urban heat island (HI) research. By retrieving urban land surface temperature and urban vegetation fraction with ASTER data, this article presents the approximate linear relation between urban land surface temperature and urban vegetation fraction. The result reveals that the ASTER images can be used in urban heat island research to reflect the temperature distribution, illustrate the heat intensity and provide macroscopic data support for the decision-making departments. The urban land surface temperature can be the most important evaluation factor in urban heat island.

In order to provide relevant data and decision support information to the Beijing Olympic Organizing Committee and International Olympic Organizing Committee, we are building up an environmental remote sensing monitoring system for Beijing 2008 Olympic Game using multiresolution, multiband, multitemporal remote sensing method and virtual reality technique. This system has four main features: 1) a 10 years long term monitoring from 1999 to 2008; 2) a stereo observation from spaceborne, airborne and ground remote sensing means; 3) a large area monitoring from Olympic game sites, Beijing area to its surrounding areas; 4) comprehensive monitoring for ecological environment, engineering construction, environmental pollution, traffic situation etc. This system serves as a huge platform for dynamic Earth observing monitoring and virtual Olympic environment with annual report and seasonal report forms. This work presents a preliminary result of constructing the system for Beijing 2008 Olympic Game.

The tectonic evolution of the India-Asia collision provides great insight to the inland deformation of Tibet plateau. There are two radically different interpretations of the development of the Tibet plateau: doubling the crust by the replacement of Tibetan upper mantle by the under thrust Indian continental crust, or by the interior shortening of the Tibet crust by the thrust fault systems in Tibet plateau. Based on the detailed field investigation, the geomorphological markers on the Landsat ETM images at different scales are used to constrain the localization, displacement along the Maergaichaka fault, Tibet, China. By the Chaoyang Lake-Maergaichak Lake, a strike slip system, including a semi-flower structure, which comprises a complex combination of the structural patterns of M-T and M-P was identified. Though there are intensive thrust related structures near the Maergaichak fault, the remote sensing and GPS results indicates that the left-lateral slip of the fault has mainly accommodated the shortening of the India-Eurasian lithospheric plates.

Synthetic aperture radar interferometry was used to study the Maergaichaka fault where Manyi earthquake occured on Nov. 8, 1997 in Tibet, China. With a more appropriate base line, we present the coseismic interferometric fringe. With the prior knowledge of strike-slip movements, the decomposition of the displacement vector in the direction of the fault strike indicates that the horizontal displacement amount to 5.03 meters near the epicenter of the Manyi earthquake, which is more consistent with the field observation (4.5 meters) than the previous work (Peltzer et al., 1999; Shan Xinjian et al., 2002). We also modeled the rock physical parameter using Okada elastic model of half space, which was characterized by a high Poisson ratio. The high Poisson ratio of the upper crust indicate the structural back grounding that it is more possible that the interior crustal shortening play an more important role in the development of Tibet plateau than the doubling the continental crust by replacement of the Tibetan upper mantle by underthrust of Indian continental crust.

There are some problems not to be resolved very well, when we merge images by the wavelet transform method. One problem is how many levels the original images should be decomposed to, in order to make fusion images obtain more information. Other problem is how to reduce the spectral distortions of the fusion images more efficiently, when we enhance the spatial resolution of the low-resolution images. We rightly select the number of wavelet decomposition levels by computing entropy of the fusion images. When performing wavelet reconstruction, we introduce the local correlation coefficient and set up the different thresholds at different levels of wavelet decomposition, in order to reduce the spectral distortions of the fusion images. In our experiment we merge three multispectral images with a panchromatic image of Quickbird data by our method. The results demonstrate that our method is a good fusion method to increase information and reduce spectral distortions.

Edge detection is important and key to image segmentation, information extraction, mapping etc. It is more difficult with SAR images than optical images, because of speckle. The aim of the paper is to show how boundaries between land and water can be detected from SAR images by using wavelet transform method, block-tracing algorithm and snake algorithm. The Ku-Band SAR image was obtained on 14 July 2003. The test area is near Huai River, located in city of Fuyang, Anhui Province. This SAR is made by Institute of Electronics, Chinese Academy of Sciences. Its azimuth resolution is 1.25 m and its range resolution is 1.2 m. To detect water edge, we apply a wavelet transform method to obtain the low-resolution image of the original image and suppress the speckle. Then we use a block-tracing algorithm to determine the coarse boundary between land and water and obtain a continuous edge. In the end, we use the snake algorithm to obtain the accurate edge. The result proves our method is a good method.

Land vegetation plays a major role in the global climate change through the carbon cycle, and climate change in turn affects vegetation growth and its photosynthetic activity. In arid and semi-arid areas, sparse vegetation cover characterizes environments. Thus quantitative temporal series analysis of vegetation distribution and its variations enables observing annual trends, and helps to find out the reason for environment variability. There are serious environmental problems, such as deforestation, soil erosion, salinization, and desert encroachment in northwestern China, its natural conditions are very delicate. In this paper, we build a time series of vegetation change by the NDVI (normalized difference vegetation index) covered northwest regions over 19 years (1982-2000), and analyze the time serial NDVI variability using three methods, which are simple differencing, slope map of NDVI, slope map of biomass. The correlation analysis between NDVI with the temperature and precipitation in northwestern China was carried out. The results showed that there was significant positive correlation between NDVI and precipitation but that temperature was not strongly correlated with NDVI in northwest China.

This paper analyzes the long sequence time series NDVI datasets and yields statistic results respectively, using simple differencing, slope map of biomass, and principle component analysis techniques. The correlation characteristics for the images of change acquired according to the algorithms mentioned above, implies that the majority pixels of the different land covers have experienced nearly the same changes in change direction and magnitude. The change of biomass of land-cover classifications between the 1980s and present are detected from the satellite data, and the land covers of the former 10 years (1981-1991) are in a better growth than the latter 10 years (1991-2001). Based on the discussion of the exotic factors affected the NDVI, like satellite shift or sensor degradation, the statistics on the slope images of biomass indicates that land cover deteriorated extensively in the past few years. The degradation of grassland or deforestation of forest regions confirmed such a fact that the status of the vegetation of the west part of China in the past 20 years (1981-2001) is suffering an extensive deterioration and only an improvement in part of the region.

ERS-l/ERS-2 synthetic aperture radar interferometry and Landsat TM was used to study the Maergaichaka fault where Manyi earthquake occured on Nov. 8, 1997 in Tibet, China. We derived an accurate digital elevation model(DEM) and the deformation interferogram of the Manyi earthquake using a tandem ERS-l/ERS-2 image pair and modeled the left- lateral slip of the fault in three dimension using half infinite elastic model. Detail geological and geomorphological offsets revalued using river valleys and structural markers on the Landsat ETM images at different scales are used to constrain the localization, total displacement at the Maergaichaka fault, Tibet China. The river network morphology associated with small rivers is offset by several meters to several kilometers along the maergaichaka fault. Our results indicates that the leftlateral slip of the fault has accommodated the shortening of the India-Eurasian lithospheric plates.

A study on efficient visualization and real-time interactivity of large-scale scenes is discussed. Introducing parallel processing technology, we present a parallelizable strategy with the pipeline algorithm, realize this parallel algorithm based on shared-memory, and then apply this program to a test site, the Peking Olympic Games planning mixed scenes, including real-time rendering, dynamical texture loading, quick browsing and so on. The results show a running performance and real-time interactivity improvement of DEPS (Digital Earth Prototype System) when using this algorithm. The parallel program of this paper was developed and running on a Silicon Graphics multiprocessor, Onyx 3200, with four MIPS R12000 processors and InfiniteReality 3 graphic accelerator, under IRIX 6.5 operating system.

Real-time simulation technologies of a large-scale virtual scene are studied. Besides the building of LOD models (including terrain and cultural features) based on a viewpoint in the Peking Olympic Games Planning Regions as a demonstration area, we adopt Active Surface Definition, a real-time framework, to manage the terrain data, apply ClipTexture to manage the high-resolution textures efficiently, and utilize parallel processing technology to deploy the scene data dynamically, which result in higher fidelity and better simulation effects in the virtual scene we finally build up.

In this study, an established microwave backscatter model is used to predict the radar backscatter behavior of rice and to understand the interaction between backscatter and the rice canopy during its growth cycle. The emphasis of this study is to understand the effect of physical plant parameters on the backscatter signatures as a function of polarization and how these signatures vary during a complete growth cycle of rice. Inputs to the backscatter model included the physical parameters of rice as obtained through field measurements. These measurements were acquired within a few days of multiple RADARSAT acquisitions of the Zhaoqing test site in southern China. RADARSAT observations and the modeling results were then compared and analyzed. The results show that the interaction mechanisms change during the nice growth cycle and that polarimetric and/or multi-polarization measurements at C-band will contribute to rice monitoring programs.

After the mosaic and geometric correction of 6 scenes of TM data, this paper analyzes the distributions of the strata exhibited in the Kalpin area, which underlie the precise interpretation of the structure. Integrated SIR-A data analysis and processing with field surveys, the information of secondary order structure and the borderline of geological bodies under shallow sediments are analyzed in this paper. The movements and the planar reappearance of the thrust sheets implies a Kalpin area strike sinistral slip of E-W and NE trend in a N-S trend compressional tectonic setting, which indicates the deformation frame and its kinematic process. Combined with the new advances of structural geology, the structure deformation features are described in detail, and strike-slip fault related models and GIS techniques are applied in the precise interpretation of the planar structures in Kalpin orogeny. Therefore, the structural shortening of sinistral slip is calculated precisely using the remote sensing data, which provide us with a new quantitative means for planar structure research.

A novel edge detection algorithm for remote sensing images, which combines the image gray level gradient and the fractal self-similarity character of image edges based on fractal theory, is introduced. The self-similarity coefficient between the element block and the local block, which are both centered at the current pixel being processed, in addition to gray level gradient decision-making criteria, are used to make the decision about the existence of image edges. A binary operator is then used to threshold its magnitude and produce the edge map of the image. The results of the experiment are presented which show the effectiveness and the noise resistance of the proposed new algorithm for remote sensing image edge detection.

Urban 3D visualization is an exciting domain, with a growing number of important applications. Researches on VR (Virtual Reality) and GIS (Geographic Information System) provide strong technological support for real-time visualization of urban scenery. This paper focuses on the interconnection and interoperability between urban 3D visualization technology and GIS, including a scheme to exchange data between the GIS database and rendering engines for visualization, navigation and manipulation. A virtual scene of IRSA (Institute of Remote Sensing Applications), based in Beijing, China, which shows a fusion between urban 3D visualization and GIS is presented and realized.

Detailed outcrop data of the bed attitudes combining with the fault-related folding theory can help interpret the seismic profiles precisely according to the algorithm of the geodesic line. This paper propose a new means to acquire the structure strike and dip data from the remote sensing data, by which constrain geological structures in three dimensions using the fault related fold models. Here, the new numerical method is introduced for the first time to measure the strike and dip of bedding using the declassified high resolution images acquired by the optical satellite Corona. The attitude data derived from the remote sensing datasets are consistent with the direct measurements in the field survey in Kuqa rejuvenated foreland basin of Tarim Basin, northwest of China, and also can be used to help interpret the seismic profiles and constrain the complex structure in the foreland basin area. Therefore, the method presented will contribute a lot to the interpretation of the seismic profiles for petroleum exploration and will play an important role in the geological exploration and prospecting, especially in the area at the initial stage of the geological reconnaissance with great advantages of low expenses and rapidity.