Object detection in synthetic aperture radar (SAR) imagery is crucial for maritime surveillance, yet it is often limited by the scarcity of large-scale annotated data. To address this challenge, this work proposes AFS-Net, a novel anchor-free domain adaptation framework for SAR ship detection. AFS-Net effectively transfers knowledge from easily annotated optical source-domain images to unlabeled SAR target-domain images, mitigating the need for expensive SAR data annotation. Unlike conventional anchor-based detectors that struggle with the diverse scales and shapes of ships, our anchor-free approach, built upon CenterNet, provides a more streamlined and accurate localization paradigm. The core of AFS-Net consists of two innovative alignment modules designed to counteract domain shifts: the Keypoint Alignment Module (KAM) and the Box Alignment Module (BAM). Specifically, KAM aligns the structural distribution of heatmap responses across domains, forcing the target domain features to mimic the ideal Gaussian-like keypoint structures from the source domain, thereby enhancing keypoint localization robustness. Concurrently, BAM aligns the center offset and size regressions to ensure the geometric consistency of the predicted bounding boxes. Experimental results show that the proposed AFS-Net outperforms existing domain adaptation object detection frameworks on SAR ship detection tasks, achieving mAP improvements of 12.10% to 33.37% over the baseline on cross-domain settings.

Remote sensing observations have the potential to improve the accuracy of temperature forecasts. However, the task of quantifying the confidence in these predictions remains challenging. Existing methods for confidence estimation, such as Bootstrap and Bayesian models, often suffer from computational inefficiencies and may impose modifications on the underlying predictor structures. To address these limitations, this letter introduces a novel and efficient confidence assessment framework for temperature forecasting, termed the second-order stationarity-based confidence assessment (SOS-CA). The proposed method is premised on the assumption that the second-order differences in temperature data adhere to a Gaussian distribution. Leveraging this assumption, SOS-CA employs statistical techniques to evaluate the Gaussianity of these second-order differences. Predictions that exhibit greater second-order stationarity are deemed to possess higher confidence. Moreover, we present a rigorous theoretical proof establishing the asymptotic equivalence of the mathematical transformations underpinning the SOS-CA methodology. To enhance its applicability, SOS-CA is extended to multiple variants to accommodate diverse forecasting scenarios. Extensive experiments using real-world remote sensing data substantiate the effectiveness of the proposed approach, demonstrating that SOS-CA achieves performance on par with or superior to existing methods while significantly reducing computational overhead.

Fusion-based hyperspectral image (HSI) super-resolution has garnered growing attention in recent years due to its strong capability in reconstructing high spatial resolution (HR) hyperspectral images. However, most existing methods either rely on a supervised training strategy or require pairwise images which are difficult to acquire. To address these issues, we propose an unsupervised and unpaired fusion-based super-resolution network, called U2SRnet, which achieves super-resolution without the aid of any supervision nor pairwise images. Instead, an arbitrary low spatial resolution (LR) HSI is used as a fixed hyperspectral input to provide reflectance information for the spectral reconstruction of RGB images. U2SRnet consists of two modules, which are spectral correction module (SCM) and band generation module (BGM) respectively. The former aims to enforce material reflectance consistency between unpaired HS-RGB images, which is implemented via a joint perception attention module (JPAM). Considering the spatial inconsistency of material positions between HS and RGB images, we propose an elaborately-designed material matching mechanism in BGM to obtain HS-RGB block pairs with high material-semantic similarity. Furthermore, a differential migration mechanism guided by physical reflectance priors, universally applicable to unpaired images, is introduced to migrate the material reflectance properties between these block pairs. Extensive experiments were performed on Cave, Harvard and TG1HRSSC datasets. Experimental results validated the effectiveness and superiority of the proposed method.

Domain adaptation methods can mitigate performance degradation in remote sensing image object detection that arises from inter-domain differences. However, current approaches often overlook the focused attention on foreground features, and the differences between foreground and background in remote sensing images implicitly diminishes the characteristics of the foreground. To address this challenge, we propose a foreground instance enhancement network (FIE-Net) to balance the differences between foreground and background in remote sensing images, while enhancing the alignment and application of foreground features. The FIE-Net cooperates the foregroundfocused multi-granularity feature alignment (FMA) module with the label filtering and application (LFA) module, progressively focusing on the salient foreground features during the processes of feature alignment and label application. Specifically, FMA directs feature alignment towards the foreground focus during the multi-granularity feature alignment process, through foregroundfocus perception attention and instance-centered emphasis approach. LFA balances the foreground and background difference information contained in labels, through the hybrid threshold label filtering method and the progressive label switching strategy. The experimental results indicate superior performance and generalization capabilities of our proposed FIE-Net in multiple remote sensing adaptation scenarios. Code is released at https://github.com/Lab-PANbin/.

Few-shot remote sensing scene classification (FSRSSC) aims to identify unseen scene classes from limited labeled samples, facing the challenge of accurately modeling data distribution and preserving image details in complex backgrounds with high intraclass variance and interclass similarity. To address this challenge, we propose a novel Diffusion Prototype Rectified Network (DiffPR-Net), which is comprised of three core modules: diffusion augmentation (DA), dual attention fusion module (DAFM) and prototype rectified module (PRM). The DA is constructed to generate high-quality remote sensing images with the objective of augmenting the training dataset. Besides, the DAFM facilitates the model to focus discriminative regions by transmitting highly fused image detail features from higher to lower layers. What’s more, the PRM addresses prototype deviation by adaptively assigning temporary labels to unlabeled data based on prediction confidence, thereby correcting the initial prototypes. Experiments indicate that our proposed method is highly promising, achieving competitive or state-of-the-art classification performance while addressing the scarcity of remotely sensed data and enhancing focus on discriminative regions.

Optical remote sensing images are inevitably affected by cloud cover. To remove clouds from optical remote sensing images, a series of deep learning-based thin cloud removal methods have been developed. However, these methods have not explored the long-range modeling ability of state space models in optical remote sensing image thin cloud removal. In this paper, we propose a frequency-domain assisted Mamba for thin cloud removal, which is called CR-Famba. In CR-Famba, to better extract global and local features of images, we design a frequency-domain assisted state space layer (FDA-SSL). The FDA-SSL consists of two core components: residual state space block (RSSB) and frequency domain detail enhancement block (FDDEB). The RSSB utilizes the visual state space module (VSSM) to extract long-range dependencies of images from a spatial perspective while adding convolutional layers to overcome local pixel forgetting. Due to the rich detailed information of remote sensing images, we present FDDEB equipped with discrete wavelet transform (DWT) to supplement the extracted local information from the frequency domain perspective. We conduct experiments on different types of cloud-containing datasets, and the results show that our method can recover images with clearer texture details compared to other methods.

Normal human heartbeat and respiratory signals are not steady sinusoidal signals and contain a multitude of harmonic components. The principle of vital sign detection using millimeter-wave radar is based on monitoring the chest cavity’s movements to obtain vital sign signals. Due to the amplitude of the respiratory signal being an order of magnitude higher than that of the heartbeat signal, the higher-order harmonics of the respiratory signal, particularly the second and third harmonics, which fall within the frequency range of the heartbeat signal, can easily overshadow it. This overlap creates difficulty in separating respiratory and cardiac signals using traditional methods. This paper proposes a heartbeat signal extraction method based on template selection, which extracts the heartbeat signal during respiratory pauses, suppresses the interference of respiratory harmonics, and experimentally validates the effectiveness of the method.

This paper presents an innovative approach for detecting human vital signs in multi-target scenarios using Frequency-Modulated Continuous-Wave (FMCW) radar within a Multiple-Input Multiple-Output (MIMO) framework. Recognizing the limitations of traditional contact-based vital signs monitoring methods, especially for sensitive groups such as infants or burn victims, our study proposes a non-invasive, radar-based solution. We develop a Linear Mixed Model (LMM) to address the challenges posed by multi-target, multi-path aliasing interference, employing Blind Source Separation (BSS) model and FastICA algorithms for signal separation and extraction. The algorithm’s effectiveness is validated through simulation experiments, where we successfully distinguish and analyze vital signs, such as heart and respiration rates, from phase-modulated Intermediate Frequency (IF) signals. The IWR6843AOP MIMO radar, operating in the 60-64GHz band, is utilized for data collection. Our results demonstrate the potential of this methodology in accurately identifying vital signs in complex, multi-target environments. Furthermore, the study suggests enhancements to the existing signal processing flow, highlighting areas for future research in real-time vital signs monitoring using FMCW radar.

This paper presents a comprehensive study on the application of Frequency-Modulated Continuous-Wave (FMCW) radar in vital sign monitoring. We identify and address the limitations of traditional signal-to-noise ratio (SNR) metrics in human vital signs detection field. Recognizing the challenges posed by the human body’s orientation, distance from the radar, and random body movements, we propose an innovative SNR estimation method. Our approach considers various real-world scenarios, including different target distances, angles, and levels of body movement, to ensure the robustness and effectiveness of vital sign detection. Through a series of experiments, we demonstrate the efficacy of our novel SNR algorithm, underscoring its superiority in different situational contexts compared to conventional methods.

Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to effectively guide the reverse generation process, without modifying the original denoising diffusion process. However, since the degraded images already include low-frequency information, starting from Gaussian white noise will result in increased sampling steps. We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images. The form of our inference process is consistent with the DDPM. We introduced a weighted residual noise, named resnoise, as the prediction target and explicitly provide the quantitative relationship between the residual term and the noise term in resnoise. By leveraging a smooth equivalence transformation, Resfusion determine the optimal acceleration step and maintains the integrity of existing noise schedules, unifying the training and inference processes. The experimental results demonstrate that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps. Furthermore, Resfusion can be easily applied to image generation and emerges with strong versatility. Our code and model are available at https://github.com/nkicsl/Resfusion.

Spectral variability is one of the challenges for hyperspectral unmixing. Recently, deep generative models are developed to describe the spectral variability, which have attracted increasing attention. However, generative unmixing methods may suffer the problems of mode collapse and image blur, which tend to generate uncontrollable endmember distribution. To address this issue, in this paper, we propose a Reversible Generative Network (Rev-Net) for hyperspectral imagery unmixing, which targets at the spectral variability challenge. Our motivation is that if the endmember distribution can be described by an explicit mathematical expression and the expression is reversible, then the generation process will be more stable. To achieve this purpose, Rev-Net mainly includes two contributions: a flow-based endmember learning module, and a theoretical proof for the reversibility of the endmember generation process. In the endmember learning module, we develop a new flow-based structure with a series of reversible transformation, so as to obtain an explicit mathematical expression for the endmember distribution. Moreover, to guarantee the existence of the explicit expression, we have theoretically proven the reversibility of the endmember learning module. Through the flow-based endmember learning module and the correspond theoretical analysis, the proposed Rev-Net can make the endmember generation process more stable and thus avoiding the problems of mode collapse and image blur. In addition, we also construct an abundance guidance module to further assist in the generation process of endmember by image reconstruction. Experimental results on real hyperspectral datasets and synthetic datasets indicate that Rev-Net has certain competitiveness. TThe codes are available at https://github.com/Lab-PANbin/Rev-Net.

Cloud removal is an inevitable task in optical remote sensing images, which aims at restoring high-quality images from cloud-contaminated images. In recent years, deep learning-based image cloud removal methods utilize convolution neural network to obtain clean images. However, due to the limitations of the convolution operator, these methods cannot effectively leverage the local and global information of the image. In this paper, we propose a Cascaded Memory Network (CMNet) for optical remote sensing imagery cloud removal. The CMNet recycles previously captured information to form a memory mechanism, which is composed of two cascaded components: local information memory module (LIMM) and global information auxiliary module (GIAM). The LIMM aims to obtain local spatial details information of the image via two sub-networks, and the GIAM tries to further restore the detail of the image from global perspective. In the LIMM, two sub-networks are constructed to capture the details from coarse to fine, each of which includes a continuous memory descriptor that describes local details of the image and a hierarchical memory correlation descriptor that adaptively integrates relevant features. In the GIAM, we design a swin cloud remove transformer layer and explore an adaptive normalization to cope with unevenly distributed thin clouds, and further provide theoretical proof for the existence of the required solution. Experimental results indicate that our method can remove clouds while maintaining the detailed information of the image. https://github.com/Lab-PANbin/.

With remote sensing images constantly being collected rapidly, class-incremental semantic segmentation task has attracted increasing attention. However, the semantic distribution shift problem of the background class in remote sensing images, which is a case of catastrophic forgetting, continues to limit available class-incremental semantic segmentation algorithms. To address this challenge, we present a new Memory-inspired Semantic Segmentation augmentation network (MiSSNet) for class-incremental learning in remote sensing images. The MiSSNet mainly includes two modules: Local Semantic Distillation (LSD) module and Class-Specific Regularization (CSR) module. LSD is a distillation structure that employs the local semantic features in retained memory to maintain correlation between pixels throughout the training process of incremental learning. It constructs a series of pixel-level correlation matrices and implicitly adjusts the semantic distribution shift problem of the background class. CSR is a class-wise regularization term that utilizes the class-specific portion of the preserved memory to help the model keep repeating the learning of the old categories. It alleviates the background classes shift problem by generating countless pixel level instances of old classes. LSD and CSR work together to tackle the semantic distribution shift problem of background class from semantic information and class information aspects, respectively. Specially, MiSSNet only needs additional single inference process for memory extraction and storage, and the whole algorithm does not add any new training parameters. Experimental results on three semantic segmentation datasets indicate the advantage of the proposed method.

Reducing interdomain discrepancies effectively enhances the performance of hyperspectral cross-scene classification tasks. However, hyperspectral single-source domain (SD) generalization methods based on mining visual representation information are significantly influenced by interdomain discrepancies. Recent research has demonstrated that frequency-domain information exhibits robust stability. Therefore, this article proposes a three-dimensional frequency domain transform network (TFTnet) for achieving hyperspectral single-SD cross-scene classification tasks. To leverage the advantageous 3-D characteristics of hyperspectral images (HSIs), all frequency domain transforms are implemented within a 3-D framework. The model consists of a generator and a discriminator. The generator incorporates a frequency domain enhancement (FDE) module and a multisource information fusion (MIF) module; the discriminator incorporates a set of weight-sharing adaptive frequency domain transform (AFT) modules. The FDE module generates the extended domain (ED) with a certain domain shift by doing linear interpolation in the amplitude interval of a single SD itself. The MIF module integrates multisource information through interdomain attention, ensuring a balanced approach between the SD and ED, thus generating the effective balance domain (BD). The AFT module empowers the discriminator to selectively acquire HSI frequency domain features, facilitating synergistic collaboration of spatial-spectral features and frequency domain features for enhanced image comprehension. Extensive experiments on three public hyperspectral datasets show the superiority of the method compared with state-of-the-art techniques.

Autoencoder unmixing is a popular deep learning-based spectral unmixing algorithm, which decomposes the mixed pixels into pure endmembers and their fractional proportions, but the existing methods cannot fully exploit the spatial correlation features of hyperspectral image (HIS). In this letter, we propose a dilated convolution extended-aggregated strategy (DEAS), which enhances the ability of autoencoder unmixing algorithms to extract spatial correlation features. This strategy constructs a module utilizing various combinations of dilated convolutions with different scales. DEAS extracts the spatial relationships within multiple ranges around each pixel. Compared with the full connection and convolution commonly used in the encoder layers, autoencoder algorithms with DEAS expand the acceptance domain of the network. Furthermore, DEAS aggregates the spatial information in different ranges to obtain the feature map fully acquiring the relationship between pixels, which improves the unmixing performance. In particular, DEAS can be inserted into the existing autoencoder unmixing algorithms to get more abundant spatial information, and the methods using DEAS can show better unmixing effects. We apply the DEAS to two autoencoder methods using full connection and convolution, respectively. Experiments indicate the competitiveness of the algorithms using this strategy in hyperspectral unmixing tasks.

Deep-learning-based methods have attracted increasing attention on hyperspectral unmixing, where the transformer models have shown promising performance. However, recently proposed deep-learning-based hyperspectral unmixing methods usually tend to directly apply visual models, while ignoring the characteristics of hyperspectral imagery. In this article, we propose a novel double-aware transformer for hyperspectral Unmixing (UnDAT), which aims at simultaneously exploiting the region homogeneity and spectral correlation of hyperspectral imagery. One of the major assumptions of UnDAT is that hyperspectral remote-sensing images involve many homogeneous regions. Pixels inside a homogeneous region usually present similar spectral features, and the edge pixels are just the reverse. Another observation is that the pixel spectra are continuous and correlated. Based on the above assumption and observation, we construct the UnDAT by developing two modules: Score-based homogeneous-aware (SHA) module and the spectral group-aware (SGA) module. In the SHA module, a feature map rearrangement (FMR) approach is proposed to split the shallow feature maps from a linear encoder into an ordered homogeneous map (HomoMap) and an edge map and develop a homogenous region-aware strategy for deep feature representation. In the SGA module, the dependency among neighboring bands is described by dividing the hyperspectral image into multiple spectral groups and calculating the spectral similarity among bands within each group. Experiments on both real and synthetic datasets indicate the effectiveness of our model. We will publish the code of our approach if the article has the honor to be accepted.

One of the major difficulties for hyperspectral imagery (HSI) classification is the hyperspectral-heterospectra, which refers to the same material presenting different spectra. Although joint spatial-spectral classification methods can relieve this problem, they may lead to falsely high accuracy because the test samples may be involved during the training process. How to address the hyperspectral-heterospectra problem remains a great challenge for pixel-wise hyperspectral imagery classification methods. Domain generalization is a promising technique that may contribute to the heterospectra problem, where the different spectra of the same material can be considered as several domains. In this paper, inspired by the theory of domain generalization, we provide a formulaic expression for hyperspectral-heterospectra. To be specific, we consider the spectra of one material as a conditional distribution and propose a domain-generalization-based method for pixel-wise HSI classification. The key of our proposed method is a new Label-indicate-Conditional-alignment (LiCa) block that focuses on aligning the spectral conditional distributions of different domains. In the LiCa block, we define two loss functions, cross-domain conditional alignment, and cross-domain entropy, to describe the heterogeneity of HSI. Moreover, we have provided the theoretical foundation for the newly-proposed loss functions, by analyzing the upper bound of classification error in any target domains. Experiments on several public data sets indicate that the LiCa block has achieved better generalization performance when compared with other pixel-wise classification methods.

Multiobjective optimization aims at addressing the conflicting objectives, which has been introduced to improve the performance of sparse hyperspectral unmixing. Recently proposed multiobjective unmixing methods usually employ evolutionary algorithms to improve the unmixing accuracy. However, evolutionary algorithms may suffer the challenge of convergence, in which case the reasonability of the solutions is hard to guarantee. To solve the problem of convergence, in this paper, we present a new gradient-based multiobjective unmixing method, which explores the optimization direction in a theoretically reliable manner. Furthermore, considering the mathematical model of hyperspectral sparse unmixing where sparsity error objective of selected endmembers is discrete, we develop a greedy hash based coding approach which is able to well describe the discrete constraints imposed on endmembers. The major components of the proposed method are a search approach and an update approach. In the search approach, we construct the pareto descent direction via a gradient-based strategy, which contributes to converging to an optimal continuous solution by searching along this direction. In the update approach, we update discrete binary endmember via hash coding under the guidance of greedy principle, which allows our method to handle the problem of discrete objective. The major contribution of the proposed method is designing a new framework that can get the optimal discrete endmembers in a convergent way. Moreover, we provide the theoretical analysis and proof for the convergence. Synthetic and real-world experiments have indicated the advantages of our algorithm when compared with evolutionary multiobjective unmixing methods.

Synthetic aperture radar (SAR) imaging has round-the-clock data acquisition capability regardless of light and climate constraints, so it has been widely used for ship detection. However, SAR images usually suffer lower imaging quality, which may result in indistinct contours and non-negligible noise. Therefore, the manual labeling for SAR images is expensive, leading to a lack of training data in the task of ship detection. In this paper, we propose a route by utilizing domain adaptive methods to transfer information from labeled visible images (source domain) to unlabeled SAR images (target domain) for ship detection. To address the distribution mismatch between domains, we develop a novel imbalanced discriminant alignment (IDA) approach to improve the discriminant ability of the network and prevent negative migration. The core of the IDA approach is applying a new loss function called imbalanced prediction consistency (IPC) loss to describe the domain classifier consistency, and we further provide theoretical analysis for the effectiveness of the IPC loss. IDA ensures consistency at the image level and instance level, and focuses on the consistency of the source domain to enhance the feature extraction capability of the adversarial network. The theoretical discussion has proven that a necessary and sufficient condition for convergence of the IPC loss is that the two discriminant probabilities converge to 0 at the discriminant distance we define. Experimental results have indicated the advantage of IDA when compared with other domain adaptation SAR ship detection methods.

Registration of multi-modal remote sensing images with geometric distortions is one of the fundamental applications, but it remains difficult since multi-modal remote sensing images have significant differences in both radiometric and geometric features. One of the challenges is the disregarding of modality-specific information, which hinders the model from focusing on the content information of structure and texture due to differences in radiometric features. In this paper, an unsupervised Content-focused Hierarchical Alignment Network (CHA-Net) is proposed, which is constructed based on the theory of domain adaptation. The kernel idea of CHA-Net is to weaken the style differences among different modal images and achieve non-rigid multi-modal remote sensing image registration. CHA-Net is a hierarchical refinement model, where different scales of features are aligned respectively by utilizing the field calibration module and gradually generating the registration field. To be specific, CHA-Net consists of two structures: the Siamese Feature Decoupling (SFD) structure and the Hierarchical Refinement Alignment (HRA) structure. The SFD aims at reducing the style differences caused by cross-modal differences and developing a shared-weight Siamese network to map images to content feature space. The HRA enhances the ability of the network by capturing global distortions based on the Transformer model. Experiments on public datasets indicate that compared with other methods, CHA-Net performs better when geometric and radiometric distortions appear.

Spectral super-resolution has attracted research attention recently, which aims to generate hyperspectral images from RGB images. However, most of the existing spectral super-resolution algorithms work in a supervised manner, requiring pairwise data for training, which is difficult to obtain. In this paper, we propose an Unmixing Guided Unsupervised Network (UnGUN), which does not require pairwise imagery to achieve unsupervised spectral super-resolution. In addition, UnGUN utilizes arbitrary other hyperspectral imagery as the guidance image to guide the reconstruction of spectral information. The UnGUN mainly includes three branches: two unmixing branches and a reconstruction branch. Hyperspectral unmixing branch and RGB unmixing branch decompose the guidance and RGB images into corresponding endmembers and abundances respectively, from which the spectral and spatial priors are extracted. Meanwhile, the reconstruction branch integrates the above spectral-spatial priors to generate a coarse hyperspectral image and then refined it. Besides, we design a discriminator to ensure that the distribution of generated image is close to the guidance hyperspectral imagery, so that the reconstructed image follows the characteristics of a real hyperspectral image. The major contribution is that we develop an unsupervised framework based on spectral unmixing, which realizes spectral super-resolution without paired hyperspectral-RGB images. Experiments demonstrate the superiority of UnGUN when compared with some SOTA methods.

Connected and automated vehicles (CAVs) have the advantages of improving road safety and traffic efficiency. This study proposes a dynamic speed control system for a two-lane scenario to reduce collision risks near freeway bottlenecks for mixed traffic flow, consisting of both CAVs and human driven vehicles (HDVs). The control system includes two major strategies, the dynamic deceleration strategy and uniform lane-changing strategy. The core idea of dynamic deceleration is to command the CAVs to slow down proactively and form moving barriers to guide the following HDVs to slow down before the bottleneck. The establishment of uniform lane-changing strategy aims to improve the uniform coefficient of CAVs for each lane in the mixed traffic flow, and three different solving methods are applied for uniform lane-changing strategy to further improve safety. Simulation experiments are designed, and the performance of the system is investigated in terms of its safety and efficiency. Sensitivity analysis has been carried out on the length of the area where uniform lane-changing strategy is implemented. The results indicate that: (1) dynamic deceleration strategy based on CAVs can effectively reduce collision risks; and (2) the uniform lane-changing strategy can further improve the performance of the dynamic deceleration strategy.

For safety applications in intelligent transportation system (ITS), it is essential for vehicles and pedestrians to decode the safety messages from nearby moving vehicles through direct sidelink in the presence of cellular link. This article presents the coverage probability analysis of vehicular safety messages-prioritized cellular vehicle-to-everything (C-V2X) communications. We model the spatial layout of macro base stations (MBSs) and vehicles as a 2-D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP). Since vehicles can be regarded as mobile base stations, we assume that the MBSs and vehicles share the same spectrum. In a similar way, we consider two kinds of users, i.e., planar users and linear users, which are also modeled by a 2-D PPP and a PLCPP, respectively. Using a stochastic geometry tool, we derive the signal-to-interference ratio (SIR)-based coverage probability of four links (i.e., downlink of planar user, sidelink of planar user, downlink of linear user, and sidelink of linear user) according to the vehicle-prioritized association scheme. We assume that the users are required to decode the vehicular safety messages if they are within a certain distance from vehicles. Then, we derive the conditioned coverage probability of four links to study their reliability. In addition, we explore the impacts of several key parameters on the coverage probability and provide some design insights.

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Fusion-based hyperspectral super-resolution (HSR) algorithms usually utilize a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (MSI) to generate a high-resolution hyperspectral image (HR-HSI), which have attracted increasing attention in recent years. However, how to deal with the abundant spectral information of hyperspectral images and complex structure characteristics of MSIs has always been the focus and difficulty of fusion-based HSR. In this article, we propose a new structure–color preserving network (SCPNet) for HSR, which is developed under the basis of the joint attention mechanism. The SCPNet mainly includes three modules: structure-preserving module (SPM), color-preserving module (CPM), and cross-fusion module. The SPM is constructed based on the spatial attention, which aims to capture and enhance the significant structure information from the high-resolution MSI. Meanwhile, the CPM is constructed based on the channel attention, where the spectral characteristics in the LR-HSI are preserved during the reconstruction process. Finally, we propose a cross attention-based cross-fusion strategy to integrate the features from the two branches and reconstruct the final HR-HSI. The major contribution of SCPNet is that the structure and color information is described and preserved via the joint attention mechanism. Experimental results indicate that the proposed SCPNet has presented advantages on three benchmark datasets when compared with some state-of-the-art HSR methods.

Recently, feature pyramid has been widely exploited in remote sensing detectors, which greatly alleviates the problem arising from scale variation across objects in remote sensing images. However, these object detectors with feature pyramid give insufficient consideration that objects in remote sensing images usually maintain symmetrical shape. To address this issue, we propose an anchor-free-based detector called Centerness-Aware Network (CANet), which could capture the symmetrical shape of objects in remote sensing images. The kernel structure of CANet is a new Centerness-Aware Model (CAM) that contains three components: Multiscale Centerness Descriptor (MSCD), Centerness Detection Head (CDH), and Feature Selective Module (FSM). Considering that symmetrical objects will maintain a rigid appearance around their center region, three components are integrated into the feature pyramid to extract and utilize the features around the center region. More precisely, the MSCD is embedded into the feature pyramid and highlights the center of current objects through the attention mechanism. Guided by the MSCD, the CDH could accurately capture the center of objects by per-pixel prediction. Furthermore, the FSM is connected to the CDH, which guides the CDH to adaptively select the optimal feature level from the pyramidal features. The selected feature level could describe the best semantic information around the center region, which helps the network progressively fit the symmetrical shape of remote sensing objects. Besides, we also design the hybrid loss function to effectively train CAM in the end-to-end way. The experiments show that our network is competitive with some state-of-the-art detection networks.

Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures (endmembers) and their proportions (abundances). Recently, deep learning-based methods have been applied to enhance the representation ability of unmixing models by extracting joint spatial–spectral characteristics of the hyperspectral data. However, most deep learning based-unmixing methods usually conduct global smoothing by convolutions on the whole hyperspectral imagery, which may ignore the variations within the imagery and result in oversmoothing. In this article, we propose a deep network for hyperspectral unmixing based on a new global–local smoothing autoencoder (GLA). GLA is an unsupervised model, which aims at exploring the local homogeneity and the global self-similarity of hyperspectral imagery. The proposed GLA network mainly includes two modules: a Local Continuous conditional random field Smoothing (LCS) module and a global recurrent smoothing (GRS) module. In LCS, we propose a conditional random field-based smoothing strategy to describe the joint spatial–spectral information within a local homogeneity region, which also reduces the risk of abundance maps boundary blurry. In GRS, we follow the self-similarity assumption for hyperspectral imagery and develop a recurrent neural network structure to exploit potential long-distance dependency relationships among pixels. The GLA is compared with several state-of-the-art unmixing methods on both real and synthetic data, and the abundance estimation results indicate that our method is promising. We will publish the code of GLA if this article has the honor to be accepted.

Ship detection from synthetic aperture radar (SAR) images is a hot topic, but the difficulty in collecting labeled SAR images may hinder the development of deep-learning-based detection methods. Inspired by the idea of domain adaptation, in this article, we propose a hierarchical similarity alignment neural network (HSANet) for ship detection in SAR images, which is a domain adaptive (DA) approach with optical remote sensing images as training samples. The kernel target of HSANet is to mine and align both the global structure and the local instance information between SAR and optical images, where two modules, structural alignment module (SAM) and prototype alignment module (PAM), are designed to, respectively, conduct two hierarchies of alignment process. In general, SAM attempts to extract the global structure similarity which exists in image-level feature representation, while PAM tends to extract the local shape similarity which is instance-level representation. To be specific, SAM is developed by Fourier-based feature alignment, which tries to describe the similar structural relationship between optical and SAR images. Meanwhile, PAM is proposed based on the conjoint confidence analysis where the instance-level ship representations of the source and target domains is aligned. SAM and PAM work together to construct a hierarchical domain adaptation network for SAR ship detection. Experiments on several public datasets may indicate the effectiveness of the proposed method.

Remote sensing image scene classification aims to automatically assign semantic labels for remote sensing images. Recently, to overcome the distribution discrepancy of training data and test data, domain adaptation has been applied to remote sensing image scene classification. Most domain adaptation approaches usually explore transferability under the assumption that the source domain and target domain have common classes. However, in real applications, new categories may appear in the target domain. Besides, only considering the transferability will degrade the classification performance due to the strong interclass similarity of remote sensing images. In this article, we present an open set domain adaptation algorithm via exploring transferability and discriminability (OSDA-ETD) for remote sensing image scene classification. To be specific, we propose the transferability technology, which aims at the high interdomain variations and high intraclass diversity of remote sensing images. The purpose of transferability is to reduce the global distribution difference of domains and the local distribution discrepancy of the same classes in different domains. For high interclass similarity in remote sensing images, we adopt the discriminability strategy. The discriminability intends to enlarge the distribution discrepancy of different classes in different domains. To further promote the effectiveness of scene classification, we integrate the transferability and the discriminability into a framework. Moreover, we prove that the algorithm has a unique optimizer.

In this paper, we present the success probability analysis of cooperative cellular vehicle-to-everything (C-V2X) communications, i.e., cellular-relay V2X communications. We model the spatial layout of macro base stations (MBSs) as a 2D Poisson point process (PPP) and roads as a Poisson line process (PLP), with road wireless nodes (including vehicles and roadside units) modeled as a 1D PPP on each road. For a typical source node, we calculate the signal-to-interference ratio (SIR)-based success probability of transmitting a packet to its closest destination node with the assist of its nearest MBS. We take into account three cooperative transmission schemes and derive their expressions of joint success probability in two consecutive phases considering the correlation of road topology and nodes’ locations, respectively. We verify the accuracy of our analytical results through Monte-Carlo simulations. In addition, we explore the impacts of several key parameters on the success probability and discuss the selection of transmission schemes.

To compensate for the limitations of existing dedicated short range communications (DSRC), cellular vehicle-to-everything (C-V2X) has been proposed recently, which is also a promising technology for future intelligent transportation systems (ITS). Using stochastic geometry approach, this paper presents the modeling and analysis of success probability in multi-relay cooperative C-V2X networks. The spatial distribution of base stations (BSs) and vehicles in$\mathbb {R}^{2}$are modeled as a 2D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP), respectively. We focus on the success probability of a source vehicle sending a message to the nearest destination vehicle assisted by the closest BS. Each vehicle is equipped with single antenna whereas each BS is equipped with multiple antennas, which act as independent relays. We consider two decoding schemes, i.e., selection combining (SC) and maximum ratio combing (MRC), and obtain the analytical expressions for joint success probability during two continuous time slots, taking into account the interference correlation (i.e., the spatial correlation of vehicle location). The analytical model is validated using Monte Carlo simulations in MATLAB, and the effects of major parameters on success probability are investigated.

It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.

Deep learning-based remote sensing object detectors are usually composed of two branches: classification and localization. Recently proposed object detectors often follow the pipeline that classification and localization branches share the same feature maps, which leads to a strong coupling relationship between them. However, when tackling remote sensing images, this strong coupling relationship may impair the performance of the detectors because the top-view perspective of remote sensing images may result in conflicts between classification and location branches. To address this issue, we propose a decoupled classification localization network (DCL-Net) by considering the different characteristics between the two branches. Two modules are developed to suppress the strong coupling: receptive field aggregation module (RFAM) and bottom-up path aggregation module (PAM). For the classification branch, RFAM can learn the relationship between objects and context information by simulating the human receptive field and improve the robustness of the classification branch to rotational distortions. For the localization branch, PAM can enhance the entire feature hierarchy by transferring the rich detailed information of low-level features, which helps the detector to achieve precise bounding box regression. Compared with existing methods, the major contribution of DCL-Net is that the independence of the classification and localization branches can be significantly enhanced, which may be beneficial to the detection accuracy for the objects in remote sensing images. Experiments on public data sets validate the effectiveness of our detector.

Sparse hyperspectral unmixing has attracted increasing investigations during the past decade. Recent research has indicated that library pruning algorithms can significantly improve the unmixing accuracies by reducing the mutual coherence of the spectral library. Inspired by the good performance of library pruning, in this article we propose a new hyperspectral unmixing algorithm which integrates the idea of library pruning and sparse representation. An obvious challenge for pruning algorithms is that the real endmembers must be preserved after pruning. Unfortunately, recent proposed pruning algorithms, such as multiple signal classification are actually prepruning strategies, which cannot guarantee that the endmembers exactly exist in the selected spectral subset when the image noise is strong. To overcome this difficulty, we develop a simultaneous optimization approach which involves the pruning operation into the optimization process. Compared with existing prepruning-based unmixing methods, the proposed algorithm can gradually compress the search space of sparse representation, which may relieve the loss of spectral information caused by the rapid compression of the library. Instead of simply designing a regularizer, in this article we utilize a multiobjective-based framework where reconstruction error, sparsity error, and the pruning projection function are considered as three parallel objectives, so as to avoid the manually settings of regularization parameters. Moreover, we have provided theoretical analysis and proof for the reasonability of our pruning objective. Experiments on synthetic hyperspectral data may indicate the superiority of the proposed method under high-noise conditions.

Remote sensing image scene classification refers to assigning specific semantic labels for remote sensing images. Due to the lack of labeled remote sensing images, domain adaptation is applied to remote sensing image scene classification. However, recent proposed methods mainly focus on the closed set scenario. In this paper, we explore the open set scenario and introduce an open set domain adaptation network (OSDANet) for remote sensing image scene classification. Inspired by the idea of Generative Adversarial Network (GAN), we design a feature generator as well as a classifier which are learnt in an adversarial way. The purpose of the classifier is to find a boundary between the source and the target samples, while the feature generator attempts to force target samples away from the boundary. Especially, for the target samples, the feature generator will determine whether to align them with source samples or reject them as unknown target samples. The experimental results have indicated the effectiveness of the proposed method.

Deep learning-based methods have presented a promising performance in the task of hyperspectral imagery classification (HSIC). However, recent methods usually are considered HSIC as a patchwise image classification problem and addressed it by giving a single label to the patch surrounding a pixel. In this letter, we propose a new semantic segmentation network that can directly label each pixel in an end-to-end manner. Compared with patchwise models, our method can significantly improve training effectiveness and reduce some manual parameters. Another challenge in HSIC is that the spatial resolution of hyperspectral imagery is relatively low; in that case, the pooling operation may result in resolution and coverage loss. To address this issue, we introduce dilated convolution to our model and construct a dilated semantic segmentation network (DSSNet). Different from some existing works, DSSNet is specially designed for HSIC without complicated architecture, and no pretrained models are required. The joint spatial-spectral information can be extracted via an end-to-end manner and, thus, avoid various preprocessing or postprocessing operations. Experiments on two public data sets have demonstrated the effectiveness of our improvements compared with some of the latest deep learning-based HSIC models.

Phase unwrapping (PU) is a critical step in interferometric synthetic aperture radar (InSAR) processing chain, which can directly affect the quality of the final results. In order to improve the accuracy and robustness of PU algorithm, especially for the interferograms that have relatively long perpendicular baselines, in this article, we propose a new 3-D minimum cost flow (MCF) algorithm based on closure phase. Different from current 3-D PU algorithms that aim to solve the unwrapped phase gradients (UPGs) among different interferograms and usually follow a 1+2D mode, this new approach establishes a mathematical constraint within the closure phases and unwraps them jointly. This constraint is based on a prior knowledge that the sums of UPGs, where the UPGs are in the azimuth/range plane whereas belongs to different interferograms in the closure phase, can be obtained before PU. The main advantage of this new approach is that neither a temporal deformation model nor a preliminary atmospheric phase calibration is required before PU. Besides, this new approach combines the MCF model and phase consistency, showing advantages in reducing the chance of phase aliasing. We implement our new approach in both continuous and discrete interferograms and use real SAR data to validate the performance by comparing with the 2-D MCF algorithm in SNAPHU and the 3-D PU algorithm in StaMPS.

Remote sensing image scene classification refers to assigning semantic labels according to the content of the remote sensing scenes. Most machine learning-based scene classification methods assume that training and testing data share the same distributions. However, in real application scenarios, this assumption is difficult to guarantee. Domain adaptation (DA) is a promising approach to address this problem by aligning the feature distribution of training and testing data. Inspired by the idea DA, in this article, we propose a correlation subspace dynamic distribution alignment (CS-DDA) method for remote sensing image scene classification. Aiming at the characteristics of remote sensing scenes, we introduce two strategies to balance the effects of source and target domains: subspace correlation maximization (SCM) and dynamic statistical distribution alignment (DSDA). On the one hand, SCM tries to avoid mapping source domain data into irrelevant subspace to preserve the representation information of the source domain. On the other hand, DSDA is proposed to reduce the data distribution discrepancy between aligned source and target domains. Specifically, DSDA is a dynamic adjustment process where an adaptive factor is learned to balance the interclass and intraclass distribution between domains. Moreover, we integrate SCM and DSDA into a uniform optimization framework, and the optimal solution can be converted to the generalized eigendecomposition problem by derivation. The experimental results indicate that the proposed method can generate better results when compared with other feature distribution alignment methods.

To overcome the shortcomings of Dedicated Short Range Communications (DSRC), cellular vehicle-to-everything (C-V2X) communications have been proposed recently, which has a variety of advantages over traditional DSRC, including longer communication range, broader coverage, greater reliability, and smooth evolution path towards 5G. In this paper, we consider an LTE-based C-V2X communications network in irregular Manhattan grids. We model the macrobase stations (MBSs) as a 2D Poisson point process (PPP) and model the roads as a Manhattan Poisson line process (MPLP), with the roadside units (RSUs) modeled as a 1D PPP on each road. As an enhancement architecture to DSRC, C-V2X communications include vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, vehicle-to-pedestrian (V2P) communication, and vehicle-to-network (V2N) communication. Since the spectrum for PC5 interface in 5.9 GHz is quite limited, cellular networks could share some channels to V2I links to improve spectral efficiency. Thus, according to Maximum Power-based Scheme, we adopt the stochastic geometry approach to compute the signal-to-interference ratio- (SIR-) based success probability of a typical vehicle that connects to an RSU or an MBS and the area spectral efficiency of the whole network over shared V2I and V2N downlink channels. In addition, we study the asymptotic characteristics of success probability and provide some design insights according to the impact of several key parameters on success probability.

In this paper, we present an algorithm to simultaneously obtain high-resolution images and segmentation maps from low-resolution inputs. Super-resolution and segmentation both are challenging task, but they may have certain relationship. Super-resolution will provide images with more details that may help to improve the segmentation accuracy, while label maps in segmentation dataset may contribute to finer edges during super-resolution process. Therefore, we aim to combine these two tasks and explore the influence for each other. For this end, we proposed a new deep neural network to simultaneously address the super-resolution and segmentation tasks for remote sensing images, which is named S2Net. The S2Net is an integrated network composed of a super-resolution sub-network and a segmentation sub-network, which is trained in an end-to-end manner. Experimental results demonstrate that this combination can enhance the performance on these two tasks.

Remote sensing imagery semantic segmentation refers to assigning a label to every pixel. Recently, deep convolutional neural networks (CNNs)-based methods have presented an impressive performance in this task. Due to the lack of sufficient labeled remote sensing images, researchers usually utilized transfer learning (TL) strategies to fine tune networks which were pretrained in huge RGB-scene data sets. Unfortunately, this manner may not work if the target images are multispectral/hyperspectral. The basic assumption of TL is that the low-level features extracted by the former layers are similar in most data sets, hence users only require to train the parameters in the last layers that are specific to different tasks. However, if one should use a pretrained deep model in RGB data for multispectral /hyperspectral imagery semantic segmentation, the structure of the input layer has to be adjusted. In this case, the first convolutional layer has to be trained using the multispectral /hyperspectral data sets which are much smaller. Apparently, the feature representation ability of the first convolutional layer will decrease and it may further harm the following layers. In this letter, we propose a new deep learning model, COpy INitialization Network (CoinNet), for multispectral imagery semantic segmentation. The major advantage of CoinNet is that it can make full use of the initial parameters in the pretrained network's first convolutional layer. Comparison experiments on a challenging multispectral data set have demonstrated the effectiveness of the proposed improvement. The demo and a trained network will be published in our homepage.

Recent studies about hyperspectral imagery (HSI) classification usually focus on extracting more representative features or combining joint spectral-spatial information. However, besides feature extraction, developing more powerful classifiers can also contribute to the accuracies of HSI classification. In this paper, we propose a multiobjective-based sparse representation classifier (MSRC) for HSI data, which mainly tries to address two problems: 1) pixel mixing and 2) lacking abundant labeled samples. MSRC is motivated by the SRC, and further integrating the idea of hyperspectral unmixing. Different from the traditional SRC-based methods, the novelty of MSRC consists of the optimization process, i.e., we directly handle the L0-norm problem without any relaxation. The sparse term is not considered as a regularization operation. Instead, we transform the problem of weight vector estimation to subset selection, and propose a multiobjective-based method to optimize the L0-norm sparse problem. The residual term and sparse term are regarded as two parallel objective functions that are optimized simultaneously. We further utilize the linear mixing model to represent test pixels based on the selected atoms. The final class labels are determined according to the abundance estimation results by nonnegative least squares. Owing to the characteristics of the multiobjective method and the binary property of the sparse solution vector, MSRC does not require too many training samples to build the dictionary. Moreover, theoretically, MSRC can be easily improved to extended version such as combining spatial information.

Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. A possible drawback of these methods is that they can only generate one solution in a single run with a given band number. To overcome this problem, multiobjective (MO)-based methods, which were able to simultaneously obtain a series of subsets with different band numbers, were investigated for band selection. However, because the range of band selection problem is discrete, recently proposed weighted Tchebycheff (WT)-based MO methods may suffer weakly Pareto optimal problem. In this case, the solutions for each band number will be nonunique and no optimal solution exists. Decision makers have to manually select a unique solution for each band number. In this paper, we provide a theoretical analysis about the weakly Pareto optimal problem in band selection, and quantitatively give the boundary conditions. Moreover, we further summarize the suggestions which will help users avoid the weakly Pareto optimal problem. According to these criteria, we develop a new adaptive-penalty-based boundary intersection (APBI) framework to improve the MO algorithm in hyperspectral band selection. APBI mainly includes two advantages: 1) avoiding weakly Pareto optimum and 2) reducing the sensibility of the penalty factor. The theoretical analysis is further validated by contrast experiments. The results demonstrate that the weakly Pareto optimal solutions really exist in WT methods, while APBI can overcome this problem.

Sparse unmixing has become a popular tool for hyperspectral imagery interpretation. It refers to finding the optimal subset of a spectral library to reconstruct the image data and further estimate the proportions of different materials. Recently, multi-objective based sparse unmixing methods have presented promising performance because of their advantages in addressing combinatorial problems. A spectral and multi-objective based sparse unmixing (SMoSU) algorithm was proposed in our previous work, which solves the decision-making problem well. However, it does not show outstanding advantages in strong noise cases. To solve the problem, in this paper, SMoSU is improved based on the estimation of distribution algorithms (EDAs). The machine learning based EDAs have been a reliable approach in solving multi-objective problems. However, most of them are for special problems and relatively weak in theoretical foundations. Thus, it is unreliable to extend it directly to sparse unmixing. Here, we improve EDA on the basis of classification and propose a classification-based model for individual generating under the framework of SMoSU (CM-MoSU). In CM-MoSU, the whole population is divided to be positive and negative. Then, the macroinformation of positive individuals is used to guide the generation of new individuals. Therefore, the optimization task could pay more attention to the feasible space with high quality. Moreover, some theoretical analyses are presented to prove the reliability of CM-MoSU. In experiments, several state-of-the-art sparse unmixing algorithms are compared. Both synthetic and real-world experiments demonstrate the effectiveness of CM-MoSU.

Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. Owing to the parameters independence and the optimal solutions, multi-objective based methods have presented promising performance. However, the characteristics of the hyperspectral band selection problem make its range to be discrete. In this case, recently proposed weighted Tchebycheff based multi-objective band selection methods could only reach the weakly Pareto optimal, which would result in non-unique solutions. In this paper, we improve the decomposition process of the multi-objective based band selection method via a boundary intersection approach. Compared with weighted Tchebycheff decomposition, the proposed method is able to change the shape of the contour lines between Pareto Front and the ideal point, and this approach is particularly suitable for discrete-range problems. The effectiveness of our improvement is demonstrated by comparison experiments.

In remote sensing images, detecting aeroplanes of special shapes is difficult due to limited number of samples. Without enough training samples, most supervised learning based algorithms will fail. Focusing on the specially-shaped aeroplanes in high-resolution optical remote sensing imagery, this paper presents a single-sample approach. The proposed approach takes one sample as input and directly searches for similar matches from the image. Unlike the supervised learning algorithms which extracts information from positive and negative samples, the hyperspectral algorithm estimates the statistics of background by analyzing the global information of the target image, needless to provide negative samples. Furthermore, this algorithm tries to find a hyperplane projected on which the background is compressed while the target is preserved, making it more data-adaptive than the conventional similarity measurements. Experiments on real data have presented the robustness of the proposed method.

Sparse representation based hyperspectral unmixing methods have attracted increasing investigations during the past decade. Recently, multiple signal classification (MUSIC) algorithm has been verified effective in reducing the mutual coherence of the spectral library. However, the popular pre-pruning strategy by MUSIC cannot guarantee that the end-members exactly exist in the selected spectral subset when the image noise is serious. In this paper, we propose a new sparse unmixing method for hyperspectral images via integrating the pruning operation into the optimization process. The projection of the library is represented by an objective function in the proposed method. To avoid the manually settings of regularization parameters, we develop a new multi-objective based method where reconstruction error, sparsity error and the projection function are considered as three parallel objectives that could be optimized simultaneously. Experimental results have indicated the superiority of the proposed method, especially in high-noise conditions.

This paper analyzes the network connectivity in vehicular networks with slight traffic interference, i.e., small‐scale traffic accident. In this paper, we develop an analytical model for highway scenarios or sparse urban scenarios, where the slight traffic interference can make the vehicles slow down rather than block the traffic flow. When a traffic accident occurs, it is necessary to inform the nearby vehicles to slow down in order to reduce congestion at the accident location. Once they pass by, they can return to the normal value. Consequently, we can divide an entire road into two or three subsections, which helps us to analyze the connectivity performance. In addition, we analyze the impact of several key parameters on connectivity probability, including vehicle arrival rate, vehicle communication range, length of road, vehicle normal speed, and safe speed. All analytical results are verified through Monte Carlo simulation experiments. The simulation results are very close to analytical results, which means that the analytical results are accurate and the analytical model we propose is effective.

In Intelligent Transportation System (ITS), the safety and non-safety related message are delivered based on the wireless communications through Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) in Vehicular Ad Hoc Networks (VANETs) environments. The connectivity performance between vehicles and infrastructures are critical for providing high-quality service. The user behavior is a critical factor for analyzing system performance in VANETs. In this paper, we analyze the influence of the user behavior and other system parameters on the connectivity probability. The results can help control and adjust the traffic on the highway to satisfy the connectivity requirement. Therefore, the user behavior can not be neglected when designing the network connectivity model.

5G and Internet of things (IOT) develop rapidly, but the major applications of IOT-wireless sensor networks(WSNs) have numerous data, resulting in serious transmission load. In order to reduce the number of transmitted packets, this paper focuses on data aggregation for WSNs and proposes a novel event-based data aggregation mechanism using both principle component analysis(PCA) and support vector regression(SVR). The proposed method first uses correlation to achieve event checker at data aggregation node. Then when the state is normal, PCA is performed for data aggregation to reduce data dimensionality. When the state is urgent, data aggregation node receives changing data and transmits the sensing data to base station instantly, meanwhile, the data aggregation node performs SVR-based prediction. According to the prediction accuracy, data aggregation node adjusts the data transmission time interval adaptively. The simulation results show that the proposed scheme reduces the amount of transmission data among base station and one or more data aggregation nodes and decreases energy consumption compared with Adaptive-PCA.

Unsupervised band selection methods usually assume specific optimization objectives, which may include band or spatial relationship. However, since one objective could only represent parts of hyperspectral characteristics, it is difficult to determine which objective is the most appropriate. In this letter, we propose a new multiobjective optimization-based band selection method, which is able to simultaneously optimize several objectives. The hyperspectral band selection is transformed into a combinational optimization problem, where each band is represented by a binary code. More importantly, to overcome the problem of unique solution selection in traditional multiobjective methods, we develop a new incorporated rank-based solution set concentration approach in the process of Tchebycheff decomposition. The performance of our method is evaluated under the application of hyperspectral imagery classification. Three recently proposed band selection methods are compared.

Joint spectral and spatial information should be fully exploited in order to achieve accurate classification results for hyperspectral images. In this paper, we propose an ensemble framework, which combines spectral and spatial information in different scales. The motivation of the proposed method derives from the basic idea: by integrating many individual learners, ensemble learning can achieve better generalization ability than a single learner. In the proposed work, the individual learners are obtained by joint spectral-spatial features generated from different scales. Specially, we develop two techniques to construct the ensemble model, namely, hierarchical guidance filtering (HGF) and matrix of spectral angle distance (mSAD). HGF and mSAD are combined via a weighted ensemble strategy. HGF is a hierarchical edge-preserving filtering operation, which could produce diverse sample sets. Meanwhile, in each hierarchy, a different spatial contextual information is extracted. With the increase of hierarchy, the pixels spectra tend smooth, while the spatial features are enhanced. Based on the outputs of HGF, a series of classifiers can be obtained. Subsequently, we define a low-rank matrix, mSAD, to measure the diversity among training samples in each hierarchy. Finally, an ensemble strategy is proposed using the obtained individual classifiers and mSAD. We term the proposed method as HiFi-We. Experiments are conducted on two popular data sets, Indian Pines and Pavia University, as well as a challenging hyperspectral data set used in 2014 Data Fusion Contest (GRSS_DFC_2014). An effectiveness analysis about the ensemble strategy is also displayed.

Recently, for the task of hyperspectral image classification, deep-learning-based methods have revealed promising performance. However, the complex network structure and the time-consuming training process have restricted their applications. In this letter, we construct a much simpler network, i.e., the nonlinear spectral-spatial network (NSSNet), for hyperspectral image classification. NSSNet is developed from the basic structure of a principal component analysis network. Nonlinear information is included in NSSNet, to generate a more discriminative feature expression. Moreover, spectral and spatial features are combined to further improve the classification accuracy. Experimental results indicate that our method achieves better performance than state-of-the-art deep-learning-based methods.

This paper presents a novel shadow detection method in remote sensing images based on edge feature description of candidate regions. Edge gradient ratio is defined and used to represent the inherent properties of shadow regions. To improve the detection result, weighted edge gradient ratio (WEGR) is addressed, where the weight of a region is determined by the number of pixels belonging to shadow in the region, and edge gradient ratio is proposed to describe the edge feature surrounding the region. Experiments and comparisons indicate that our method achieves better accuracy both on high and low quality remote sensing images.

ABSTRACT It is very difficult to deform flexible objects in computer animation. This paper presents a novel approach to address this problem. A detail‐sensitive and deformation‐sensitive simplification is first conducted on the original mesh. The simplified mesh is then deformed, and this deformation is transferred to the original mesh to produce an initial result. Because of the discontinuity between some vertices, an as‐rigid‐as‐possible optimization is employed to prevent the shape distortion and control the surface stiffness. Various experimental data demonstrate that our algorithm is intuitive, efficient, and effective in deforming large meshes. Copyright © 2014 John Wiley & Sons, Ltd.

In pervasive computing environment, due to the mobility feature of mobile terminals, the mobile service needs to dynamically adapt execution behavior to the changing computing environment as mobile user moves. However, previous researches mainly focused on deploying a service adaption module on mobile terminals or local central server to support the adaptive execution of mobile services, which brings huge overhead to mobile terminals or can hardly meet user's personalized requirements. Therefore, we propose a cloud based framework, called CAMSPF, which includes three parts: RMC (resource management cloud), AMSPC (adaptive mobile service provision cloud), and MSM (mobile service middleware). The CAMSPF deploys the service resources in RMC for realizing efficient resource management and provision, and constructs a PMSAA (private mobile service adaption agent) for each mobile user in AMSPC in order to efficiently support personalized adaptive execution of mobile service. In addition, the MCM is a lightweight software installed on mobile terminals by which CAMSPF can collect user's realtime context and monitor service request from mobile user. Our prototype implementation of CAMSPF verifies that the adaptive execution of mobile services can be performed more efficiently than other traditional approaches, with lower energy consumption on mobile terminals.

ABSTRACT Creating an attractive mesh animation is a laborious and time‐consuming task. In this paper, we propose a practical deformation transfer algorithm to make it easier. To achieve a robust numerical solver, we perform the transfer process in the dual domain; that is, the deformations are transferred between the dual meshes of the source and target meshes. Firstly, the source animation is analyzed and visualized to help the user specify markers in the large deformation regions. Then, through respecting the coherence information, a fast and deformation‐aware surface correspondence approach is presented to determine how the source animation is transferred. Finally, the transferred result can be reconstructed via dual Laplacian optimization. Various experimental results demonstrate the effectiveness and applicability of this paper. Moreover, a user study is carefully designed to perceptually validate our motivation and advantages. Copyright © 2012 John Wiley & Sons, Ltd.

While many approaches have been developed to visualize 3D city scenes, most of them exhibit the visualization results in a uniform rendering style. This paper presents an expressive rendering approach for visualizing large-scale 3D city scenes with various rendering styles integrated in a seamless way. Each view is actually a combination of the photorealistic rendering, the nonphotorealistic rendering, and the line drawing, so as to highlight the information that is interesting for the users and de-emphasize the other that is less important. At run-time, the users are allowed to specify their interested locations with pre-determined 3D landmarks. Our system automatically computes the salience of each location and visualize the entire scene with emphasis in the area of interests. The GPU-based implementation enables real-time performance, and demonstrates outstanding practicality.

Many applications involve map visualization. Nevertheless, almost all of them display the map in the same style, which often cause information overloading. In this paper, we present a novel user-centered 2.5D focus context map visualization technique. Instead of treating all the information equally, we can custom the map according to user requirements. The main contributions of our technique are in three aspects. Firstly, our system can automatically construct a hierarchical representation of the city according to the focus point of the users by using the R-trees data structure, then presenting users the map in multiple rendering styles, highlighting the user concerned information and deemphasizing the less important information. Secondly, a landmark margin is automatically added to the original map according to the points-of-interest(POI), which illustrates the context information and enables users to maintain a macro view of the city. Moreover, by applying the 2.5D technique, we can reduce the amount of data and accelerate the transport of the information. The results show that our user-centered map visualization technique can greatly improve the efficiency for users to get their concerned information.

In this paper, we propose a robust deformation transfer algorithm. Unlike previous work, the transfer process is performed in the dual domain, i.e., the deformations are transferred between the dual meshes of the source and target meshes. Firstly, the source mesh sequence and the target mesh are converted to their dual meshes. Then the source animation is analyzed and represented as a set of affine transformations. After the surface correspondence is built, the transferred result can be reconstructed by dual Laplacian optimization. The experimental results demonstrate that our algorithm is simple but very effective.

InSAR (synthetic aperture radar interferometry) is currently a hot topic that is rapidly evolving thanks to the spectacular results achieved in various fields, especially in the construction of digital elevation models (DEMs) of the Earth's surface. This paper goes deep into the interference imagery mechanism, and from the application requirement of surveying and mapping, stresses solving InSAR key technology. A phase unwrapping method based on the principle of mean field annealing (MFA) is put forward. Finally, we testing this algorithm on some InSAR data, the result shows that this algorithm can obtain DEM correctly and efficiently. Then we analysis the result and display it through 3D. The display effect looks better.