Change detection (CD) in heterogeneous remote sensing images plays a crucial role in earth observation tasks, such as disaster monitoring and destruction assessment. Recent advancements in heterogeneous CD studies have substantially enhanced the capability to detect changes, but existing methodologies frequently lack effective control mechanisms for increasing false alarms when facing different heterogeneous scenes. Consequently, even with a high detection rate for changes, the real changes co-exist with lots of false alarms, thereby reducing the reliability and practical utility of the CD results. To address this issue, inspired by the insight of adaptive thresholding for false alarm control in constant false alarm rate (CFAR) detection, we propose a copula theory-based CD framework, named FAR-Aware-Copula-CD, to control false alarm rate (FAR) in heterogeneous CD. In the proposed FAR-Aware-Copula-CD, the heterogeneous CD problem is represented as a binary hypothesis testing problem. Then, the binary hypothesis testing problem is solved by a generalized likelihood ratio test based on copula theory, which effectively characterizes change statistics based on superpixel-level dependence within various heterogeneous image pairs. Finally, the decision thresholds of the copula-based change statistics are determined so as to satisfy the FAR constraint and ensure that the final CD result approaches a prespecified false alarm rate. Our FAR-Aware-Copula-CD provides a new approach for implementing controllable false alarms in heterogeneous CD tasks. Experimental results on four real-world datasets demonstrate the effectiveness of our proposed method.

Pan-sharpening is a commonly employed strategy to obtain high-resolution multispectral (HRMS) images. Existing deep unfolding networks for pan-sharpening suffer from ineffectively establishing the relationship between panchromatic (PAN) images and generated noisy HRMS (GN-HRMS) images in PAN-guided image denoising, lacking the support of physical models. In this paper, we first design a degradation-fusion-aware unfolding framework (DF-UF) to separate the processing of PAN-prior in PAN-guided image denoising into an individual module, PAN-prior processor, for better integrating physical models. Then, we derive a flexible intensity-hue-saturation (F-IHS) to act as the PAN-prior processor, which models the relationship between PAN images and GN-HRMS images in terms of intensity components through the intensity-hue-saturation (IHS) theory. Finally, plugging F-IHS into DF-UF, we propose a degradation-intensity-aware unfolding transformer (DIUT) to address the problem of incomplete utilization of PAN images in the denoising process. Extensive experiments on diverse scenes show that the performance of DIUT surpasses existing state-of-the-art methods.

Remote sensing scene classification is a crucial task for remote sensing image interpretation. Existing multispectral scene classification methods have overlooked the interrelationships between different spectral bands, which limits the mining of complementary information within the images. Addressing this issue, we propose a grouped cross-band fusion (GCBF) network for remote sensing multispectral scene classification to take full advantage of complementary information between various spectral bands. Firstly, we separate the various bands of the given multispectral image into different groups to better capture the characteristics of each spectral band. Then, we use the existing UniFormer as a feature extractor to learn the representations of red, green, and blue (RGB) bands. For the spectral bands other than RGB, we propose a new network called multi-stage grouped spectral feature extraction (MGSFE) network to learn discriminative representations. We also draw inspiration from the band combination in the field of remote sensing and introduce a cross-band attention fusion (CBAF) module designed to adaptively merge features from both the RGB bands and other spectral bands. Extensive experiments on three widely used remote sensing multispectral scene classification datasets of BigEarthNet, SEN12MS, and EuroSAT demonstrate the superiority of our proposed method compared with several state-of-the-art (SOTA) methods.

Change detection (CD) in heterogeneous remote sensing images has been widely used for disaster monitoring and land-use management. In the past decade, the heterogeneous CD problem has significantly benefited from the development of deep neural networks (DNNs). However, the purely data-driven DNNs perform like a black box where the lack of interpretability limits the trustworthiness and controllability of DNNs in most practical CD applications. As a powerful knowledge-driven tool, copula theory performs well in modeling dependence among random variables. To enhance the interpretability of existing neural networks for heterogeneous CD, we propose a knowledge-data-driven heterogeneous CD method based on a copula-guided neural network, named NN-Copula-CD. In our NN-Copula-CD, the mathematical characteristics of copula are employed as the loss functions to supervise a neural network to learn the dependence between bi-temporal heterogeneous superpixel pairs, and then the changed regions are identified via binary classification based on the degrees of dependence of all the superpixel pairs in the bi-temporal images. We conduct in-depth experiments on four datasets with heterogeneous images, including synthetic aperture radar (SAR), multispectral, and near-infrared images, where quantitative and visual results demonstrate the effectiveness and interpretability of our proposed NN-Copula-CD method.

Recently, convolution neural networks (CNNs) have been extensively utilized in synthetic aperture radar (SAR) ship detection owing to their strong feature extraction and representation capability. However, existing CNN-based SAR ship detectors often suffer from poor sensitivity to small-scale ship targets due to the limited extractable features, especially in complex inshore scenarios. Moreover, the hand-designed components like nonmaximum suppression (NMS) calculation and anchor generation imposed in CNN-based detector significantly affect their robustness. In the face of these challenges, a novel end-to-end (E2E) transformer-based detection framework for small-scale ship targets in SAR images, named detection transformer (DETR) with improved de-noising (DN) anchor box (DINO) with refined DN and box (RDB-DINO), is proposed in this article. First, we introduce a complete contrastive DN (CCD) training technique which reconstructs and exploits different kinds of noised queries to reduce the confusion between small ships and complex backgrounds. Second, a look twice toward maximum (LTTM) algorithm for iterative box refinement is designed to mine the abnormal sample information and obtain abundant features of small ships in the training process. Finally, substantial experiments conducted on two widely used open SAR ship datasets demonstrate that the proposed approach yields superior results in small ship detection performance, outperforming prevailing state-of-the-art (SOTA) benchmarks.

A key issue of existing deep-learning-based object detection methods in remote sensing images is that they often struggle to differentiate the background and small object regions due to multi-level downsampling operations therein. Downsampling operations help extract high-level semantic features but result in excessive loss of spatial features of small objects. In this paper, we propose a new small object detector using multispectral remote sensing images, named content-aware downsampling-based detection network (CADDN), where we newly design a content-aware downsampling-based module (CADM). Unlike conventional downsampling operations that apply uniform downsampling parameters across the entire feature map, CADM adaptively assigns higher weights to feature elements that are critical for distinguishing objects from the background, and this assignment is guided by the contextual awareness of object locations during the downsampling process. Experiments based on multispectral remote sensing images with small ships and vehicles demonstrate that CADM can accurately identify and preserve the locations of important object-related features, and CADDN correspondingly achieves superior small object detection performance than state-of-the-art methods.

The fusion module of RGB and infrared (IR) remote sensing images is the key of multispectral ship detection. Existing works have shown that the cross-attention-based feature fusion can achieve good performance by extracting the complementary information of RGB and IR modalities. However, the existing commonly used cross-attention mechanisms introduce lots of redundancy parameters and mainly focus on global feature interaction of multispectral images, ignoring local detail information that is also important for small ship detection. In this paper, we propose a novel multispectral ship detection approach named LoGFusion. In LoGFusion, we design the cross stage partial module with partial convolution (CSPMPC) to reduce feature redundancy and utilize the local cross-modal fusion module (LoCFM) and global cross-modal fusion module (GCFM) to capture both local and global cross-modal features. Furthermore, we introduce a Multispectral Small Ship Dataset (MSSD) containing over 5k ship targets for small target detection. Experiments on MSSD validate the effectiveness of our method in terms of small ship detection in multispectral images.

Existing convolution neural network (CNN)-based synthetic aperture radar (SAR) ship detectors often suffer from poor performance to small-scale ship targets due to the scarcity of extractable features and the bottleneck of local receptive field in the CNN framework. To address the challenges, we propose a novel end-to-end transformer-based detection network for small-scale ship targets in SAR images, named DINO with Refined Denoising and Box (R2DB-DINO). First, we propose a complete contrastive denoising (CCD) training technique which can reconstruct and exploit various types of noisy queries to alleviate the confusion between small ships and background. Second, a look twice towards maximum (LTTM) algorithm for iterative box refinement is devised to acquire abundant features of prediction boxes for small ships by enhancing gradient information. Experiments conducted on measured dataset demonstrate the superiority of the proposed method in small-scale ship detection compared with existing methods.

Multispectral satellite images (MSSIs) offer abundant information crucial for water quality monitoring, particularly for sandy water mapping. This paper proposes a multi-band fusion (MBF) method for sandy water extent mapping (SWEM) using multi-source MSSIs. Compared with other ground surface objects, the reflectance intensity differences (RIDs) of sandy water areas are more significant between red and blue bands, as well as between near-infrared and green bands. First, we calculate the RIDs among data of the aforementioned four bands of original MSSIs. Second, we normalize and fuse RIDs, where sandy and non-sandy water areas have prominent distribution differences. The final SWEM results are obtained via the hierarchical fuzzy C- means clustering method. The robustness and superiority of our proposed MBF method for SWEM missions are validated through experimental results derived from measured MSSIs.

Existing satellite remote sensing change detection (CD) methods often crop large-scale bi-temporal image pairs into small patch pairs and use pixel-level CD methods to fairly process all the patch pairs. However, due to the sparsity of changed areas, existing pixel-level CD methods suffer from a waste of computational cost and memory resources on many unchanged areas, which hinders the deployment of the CD model on on-board platforms with extremely limited resources. To address this issue, we propose a lightweight patch-level CD network (LPCDNet) to rapidly remove the unchanged patch pairs in large-scale bi-temporal optical image pairs, which is helpful to accelerate the subsequent pixel-level processing and reduce its memory costs. In LPCDNet, based on the multi-scale max-pooling structure, the multilayer feature compression (MLFC) module is designed to compress and fuse the multi-level feature information from backbone network. Moreover, a sensitivity-guided network pruning method is proposed to remove unimportant channels and construct a lightweight backbone network based on ResNet18. Experiments on two datasets demonstrate the effectiveness and efficiency of our proposed method compared with existing methods.

As the number of remote sensing (RS) images increases, it is crucial to retrieval ship targets according to specific demands. The existing ship image retrieval methods only extract features from the image modality, which may not fully utilize the rich text information available and ignore the high-level hierarchical relations between ship classes. In this paper, we propose a language-assisted siamese contrastive framework, namely LASCF, for fine-grained ship retrieval in RS images. In the new LASCF, the siamese vision models are employed to measure the similarity between images. Moreover, a label text encoder with a pretrained language model is designed to extract the high-level semantic information from labels, and thus the information of the hierarchical relations between ship classes are fused in LASCF. Finally, the multimodal similarity measurement module based on contrastive learning is proposed to optimize the siamese vision models. The experimental results show that the proposed LASCF outperforms several existing state-of-the-art methods.

Existing deep neural network (DNN)-based target detectors in remote sensing images (RSIs) often face challenges in distinguishing small targets from the background. This is mainly because the downsampling process in DNN-based target detectors results in excessive loss of small-target-related features. This paper proposes a new small target detector in RSIs named content-perception downsampling-based target detector (CPDTD), where a novel content-perception downsampling module (CPDM) is designed to replace standard downsampling methods (e.g. pooling and convolution with stride greater than 1). CPDM encodes the input feature map and predicts the location of important features that distinguish targets from backgrounds, assigning larger weights to critical features according to the perception of the position of targets in the content during the downsampling process. Experiments on measured multispectral RSIs regarding small ship and vehicle targets demonstrate the superiorities of our proposed CPDTD in comparison with existing methods.

This paper proposes a lightweight Siamese network based on the online all-integer-pruning (OAIP) training strategy for efficient change detection in heterogeneous remote sensing images. OAIP training strategy efficiently quantizes parameters to integers and prunes insignificant weights in the filter level based on the L1-norm criterion to reduce memory usage and accelerate the online training process for change detection. Experimental results based on measured heterogeneous remote sensing images demonstrate that our proposed method provides comparable change detection performance with higher efficiency compared with state-of- the-art methods.

In this letter, we propose an efficient flood detection (EFD) method using multisource satellite images based on the algorithm–hardware co-design strategy. This method aims to improve flood detection efficiency in resource-constrained edge computing environments. First, a hybrid heterogeneous computing platform is designed to incorporate central processing units (CPUs), graphics processing units (GPUs), and field programmable gate arrays (FPGAs) hardware units to combine their individual advantages for efficient satellite image processing during the flood detection process. Second, the different flood detection algorithm modules (containing convolutional neural networks and information fusion operations) are designed and assigned to appropriate hardware units based on the characteristics of each algorithm module and the capabilities of each hardware, to reduce hardware computation waste during the operation of flood detection algorithms. Experimental results based on measured data from four flood events demonstrate that our proposed flood detection method achieves a significant improvement in computational efficiency without a noticeable loss in flood detection accuracy compared with existing state-of-the-art methods.

Most of existing studies regarding cross-modal content-based remote sensing image retrieval (CM-CBRSIR) focus on reducing/enlarging the Euclidean distances of cross modal (CM) data with the same/different content in a common feature space. The advantages of using Euclidean distance lie in its straightforwardness. However, the Euclidean distances of CM data features are sensitive to the outlier data and may lead to non-robust retrieval performance, particularly in the case of noisy images with low-quality. To address this issue, we propose a robust Hirschfeld–Gebelein–Rényi maximal correlation (HGRMC) augmented algorithm for CM-CBRSIR in this work, named by HAC. In HAC, not only the projected features of CM data in Euclidean distance space but also maximal correlation information of HGRMC are learned during the training phase of the retrieval model, where HGRMC is additionally used to capture the statistical dependency between CM data to enhance the retrieval performance with the strongly noisy input data. In the retrieval phase, we also develop a fusion scheme based on the Dempster-Shafer (DS) evidence theory to combine the superiorities of Euclidean distance and HGRMC correlation criterions. Extensive experimental results demonstrate that our proposed HAC algorithm provides better and more robust retrieval performance in comparison with existing state-of-the-art CM-CBRSIR methods.

Existing satellite remote sensing change detection (CD) methods often crop large-scale bi-temporal image pairs into small patch pairs and then use pixel-level CD methods for fair processing. However, due to the sparsity of change, existing pixel-level methods suffer from a waste of computational cost and memory resources on many unchanged areas, which reduces the processing efficiency on hardware platforms with extremely limited computation and memory resources. To address this issue, we propose a lightweight patch-level CD network (LPCDNet) to rapidly remove lots of unchanged patch pairs in large-scale bi-temporal optical image pairs, helping to accelerate the subsequent pixel-level CD process and reduce memory cost. In our LPCDNet, a sensitivity-guided network pruning method is proposed to remove unimportant channels and construct the lightweight backbone network on basis of the ResNet18 network. Then, the multi-layer feature compression (MLFC) module with multi-scale max-pooling structure is designed to compress and fuse the multi-level feature information of image patches. The output of MLFC module is fed into the fully-connected decision network to generate the predicted binary label. Finally, a weighted cross-entropy loss is utilized in the training process to tackle the change/unchanged class imbalance problem. Experiments on two CD datasets demonstrate that our LPCDNet achieves more than 1000 frames per second on an edge computation platform, i.e., NVIDIA Jetson AGX Orin, which is more than 3 times that of the existing methods without noticeable performance loss. In addition, the computational cost of the pixel-level CD processing stage can be reduced by more than 60%.

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information is exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods.

The geometrical theory of diffraction (GTD) has been widely investigated to describe the target scattering behaviors with the low-frequency ultra-wideband (LFW) radar. In this paper, we propose a new model-based deep learning method for GTD parameter estimation. The proposed method is designed by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) into a deep neural network. Unlike existing methods based on compressed sensing (CS), the key parameters in our algorithm are fully learnable, avoiding nontrivial parameter tuning procedures. Our network with simple convolution operations is more computationally efficient than existing methods, which require matrix inversions or quadratic programming and have low convergence speed. A novel loss function is designed for the new network to improve the capacity of target enhancement. Experiments on simulation data show that the new method achieves higher computational efficiency while maintaining or improving the precision of GTD parameter estimation compared with existing methods.

In this paper, we consider the problem of change detection in heterogeneous remote sensing images. Existing deep learning-based methods for change detection often utilize square convolution receptive fields, which do not sufficiently exploit the contextual information in heterogeneous images. Square receptive fields reduce the robustness to change detection scenarios with complex contextual structures, increase the number of false alarms, and degrade the performance of change detection. To address the aforementioned issue, we propose an unsupervised Siamese superpixel-based network (US2N) for change detection in heterogeneous remote sensing images. Our newly proposed method innovatively combines superpixels with the square receptive fields to generate the boundary adherence receptive fields and better capture the contextual information than existing methods only with the regular square receptive fields. Experiments based on two real data sets demonstrate that the proposed method achieves higher accuracy than other commonly used change detection methods in heterogeneous remote sensing images.

In this paper, we proposed a novel Label Augmentation network based on Self-Distillation (LASDet) for inshore ship detection in synthetic aperture radar (SAR) images. Different from canonical convolution neural network (CNN)-based approaches under the guidance of hard label, the new semisoft labels produced by self-distillation are leveraged for ship detection to boost the information of negative sample in complex scenarios. Additionally, an angle-related balance intersection-over-union (ArBIoU) loss criterion is developed to alleviate ambiguity expression of inshore ship targets by using the adaptive weight association of the aspect ratio difference and the center point deviation in regression. Experimental results on open datasets demonstrate the superiority of the proposed method compared with the existing commonly used network, especially in complex inshore scenarios.

Multi-source heterogeneous satellite image time series (MSH-SITS) have become a robust way to acquire flood detection results thanks to their high-resolution and wide coverage areas advantages. In this paper, we propose an extremely lightweight and soft fusion-based Unet (LSFUnet) architecture for flood detection based on MSH-SITS to improve the computation efficiency of existing methods. Specifically, we build the encoder-decoder module with lightweight residual blocks using a reduced number of convolution layers and smaller convolution kernel size to accelerate our flood detection method. To compensate for the performance loss caused by lightweight operations, we utilize the historical flood information as the long-term and short-term constraints on the current flood detection results. Experimental results using Gaofen-1, Gaofen-3, Gaofen-6, Huanjing-2, Sentinel-1, and Sentinel-2 satellite images demonstrate the efficiency and effectiveness of our proposed LSFUnet method.

Most existing methods for cross-modal content-based remote sensing image retrieval (CM-CBRSIR) have only focused on implementations by optimizing the projected features in a common space under the Euclidean distance criterion. In this work, to better bridge the heterogeneity gap caused by the modality difference, we propose a Hirschfeld–Gebelein–Rényi (HGR) maximal correlation augmented CM-CBRSIR method by utilizing the HGR maximal correlation between different modalities. Except for optimizing the projected features under the Euclidean distance constraints, another feature projection, which carries the information of the HGR maximal correlation, is learned during the training phase. In the retrieval phase, we combine the information learned by the Euclidean distance criterion and HGR maximal correlation based on the Dempster–Shafer (DS) evidence theory. Experimental results show that the proposed method outperforms the existing state-of-the-art methods.1

The detection of densely distributed ship targets is one of the hot issues in the context of convolutional neural network (CNN)-based synthetic aperture radar (SAR) image processing. In this case, the bounding boxes of the ships may overlap with each other. Traditional detectors do not specifically consider the processing of overlapping areas, resulting in low detection performance. To address this problem, we proposed a new SAR ship detector, where classification confidence score-based method is developed to consider the centrality prior information among the overlap areas. Then, in the shallow layers of the network, the auxiliary heads are used to guide the network to learn the features related to centers of ships. Experimental results on the open datasets with dense ships show that our method achieves the better detection performance without the obvious increase of computation burden compared with the current state-of-the-art detectors.

Accurate estimation of the sizes of ship targets plays a critical role in the task of ship classification in synthetic aperture radar (SAR) images. Existing deep neural networks (DNNs)-based methods for SAR ship size estimation (SSE) often adopt a fully connected structure that has limited capability in accurately modeling the relationships of features extracted from SAR images, leading to degraded performance of size estimation. It has been demonstrated that capsule networks provide new guidelines to capture relationships of image features by replacing traditional neurons with capsules, where the dynamic routing strategy is used to calculate correlations among capsules. In this letter, we propose an improved method for SAR SSE based on the capsule network named Caps-SSE network (SSENet). In our Caps-SSENet, a capsule-neural-mixing size mapping module is designed to transform the extracted image features into capsules and complete the estimation of ship sizes using informative feature correlations from dynamic routing. In addition, an average scaled mean square error (ASMSE) loss is proposed to improve the size estimation performance of small ships. Experimental results based on measured SAR data show that the proposed method reduces the estimation error of ship sizes in SAR images in comparison with the existing state-of-the-art method.

This paper considers the problem of change detection (CD) with multi-sensor optical remote sensing (RS) images. Copulas are adopted to characterize the dependence structure between the image pair. For this problem, a conditional copula-based CD technique has been proposed in the literature. However, in this technique, it is difficult to select the best copula function in an analytical framework. Resulting copula misspecification may lead to performance degradation. To deal with this problem, we model the CD problem as a binary hypothesis testing problem and propose a new superpixel-level copula-based statistical method (SCOPS) for CD, where an explicit strategy for copula selection is provided for the proposed method. The effectiveness of the copula selection strategy is verified on CD tasks with simulated multi-sensor optical RS images. Experiments on real RS datasets demonstrate the superiority of SCOPS over the state-of-the-art methods.

Change detection (CD) in optical remote sensing images has significantly benefited from the development of deep convolutional neural networks (CNNs) due to their strong capability of local modeling in bi-temporal images. In addition, the recent rise of transformer modules leads to the improvement of global feature extraction of bi-temporal remote sensing images. Note that the existing simple cascade of deep CNNs and transformer modules shows limited CD performance on small changed areas due to deficiencies of multi-scale information therein. To address the aforementioned issue, we propose a new CNN-transformer network (ConvTransNet) with multi-scale framework to better exploit global-local information in optical remote sensing images. In our ConvTransNet, we propose the parallel-branch ConvTrans block as the basic component to generate global-local features, i.e., adaptively integrates the global features summarized by a transformer-based branch and the local features extracted by a convolution-based branch, providing better identifiability between changed areas and unchanged areas. By fusing multiple global-local features with different scales, our ConvTransNet improves the robustness of the CD performance on changed areas with different sizes, especially small changed areas. Experiments on two public change detection datasets of optical remote sensing images, i.e., LEVIR-CD and CDD, demonstrate that our ConvTransNet achieves enhanced CD performance than the other commonly used methods.

With the soaring development of deep learning (DL) mechanisms in recent years, convolution neural network (CNN)-based methods have been extensively investigated to achieve high accuracy of ship detection in Synthetic Aperture Radar (SAR) images. However, existing CNN-based SAR ship detection methods still suffer from challenges in complex inshore scenarios due to the strong interference therein. To tackle this issue, a novel Semi-Soft Label-guided network based on Self-Distillation (SD) for SAR ship detection (S2LSDNet) is proposed in this article. First, different from the existing CNN-based detectors to extract features from the image domain only under the guidance of one-hot label, an efficient SD training strategy is devised to extract semi-soft label information to boost the inshore ship detection accuracy. Second, an angle-related and Balanced Intersection-over-Union (ArBIoU) loss is developed to enhance the inshore ship positioning performance by using the adaptive weights of center point bias and the aspect ratio difference. Experiments on the open SAR ship detection datasets demonstrate the effectiveness and superiority of the proposed method compared with the existing state-of-the-art approaches, especially in inshore scenes.

Flood monitoring is of crucial importance for protecting lives and properties. Change detection (CD) methods on multisource remote sensing images have been widely used for flood extent monitoring. In this article, we propose a spatiotemporal fusion CD (STFCD) algorithm, exploiting the spatial dependence and temporal interaction of multisource heterogeneous (MSH) satellite image time series (SITS), to realize improved flood CD performance in comparison with existing methods. The proposed STFCD algorithm mainly contains two steps, i.e., spatial clustering and temporal fusion (TF). In the spatial clustering step, we propose a sparse Markov random field (MRF)-based strategy to exploit contextually spatial features in each image of MSH-SITS, which provides a larger local receptive field than the commonly used MRF. In the TF step, the historical information of flood detection results is employed as constraints to effectively reduce the effects of terrain shadows in synthetic aperture radar (SAR) images and cloud shadows and topography shadows in optical images on flood CD results of existing methods in accordance with the temporal dependence among MSH-SITS. Experiments on real MSH-SITS (containing Gaofen-1, Gaofen-3, Gaofen-6, Sentinel-1, and Sentinel-2 satellite images) covering Chinese Amur and Huma Rivers show that the overall flood CD accuracy of our proposed STFCD algorithm is higher than the other commonly used algorithms for CD of flood extents and demonstrate the robustness of our proposed STFCD algorithm.

Convolutional neural networks (CNNs) have been widely applied in the context of ship detection in synthetic aperture radar (SAR) images, but the detection performance is still not ideal in scenarios with clutter interference. Mining frequency-domain information to suppress the sea clutter in SAR ship detection has attracted wide attention. However, existing frequency-domain ship detection methods do not process frequency-domain information adaptively, which results in the degradation of ship detection performance. To overcome this problem, this article proposes a novel deep learning network called YOLO-FA. YOLO-FA contains the proposed frequency attention module (FAM), which can process frequency-domain information of SAR images adaptively. The proposed method can suppress the sea clutter in the SAR images with the help of frequency-domain information. We evaluate the proposed method YOLO-FA on two datasets, i.e., the high-resolution SAR images’ dataset (HRSID) and SAR ship detection dataset (SSDD). Compared with the baseline method YOLOv5 and the existing commonly used methods, YOLO-FA achieves state-of-the-art detection performance on both the datasets.

Image matching is a primary technology for optical and synthetic aperture radar (SAR) image fusion but often shows limited performance due to the highly nonlinear differences between optical and SAR modalities. Recently, deep neural networks (DNNs) have been investigated to effectively extract nonlinear features for image matching tasks, where DNNs are trained based on the elaborated design of loss functions and a low loss value is often expected to obtain better image matching performance. In this letter, we first theoretically demonstrate that when the value of a state-of-the-art loss function decreases, the corresponding matching performance may not consistently improve due to the imbalanced effect of positive and negative samples. To tackle this issue, we proposed an improved loss function to train DNNs for image matching of SAR and optical images. We theoretically prove that the improved loss function ensures the improvement of the matching performance when the loss value decreases based on Taylor’s series expansion analysis. Experimental results on an open dataset with extensive optical and SAR image pairs show that 1) the proposed loss function is better than the original one in terms of image matching performance and 2) the combination of our loss function and existing multiscale convolutional gradient feature (MCGF)-based network provides better matching performance than other state-of-art approaches.

Existing ship detection approaches in optical remote sensing images often suffer from bottlenecks in inshore scenarios due to the substantial interference. In addition, the ship targets with different orientation angles and large aspect ratios increase the difficulty to accurately profile and locate them in optical remote sensing images. To address the aforementioned issues, a novel dual separation attention network (DSA-Net) based on the skew complete intersection-over-union (SkewCIoU) loss is proposed in this letter. In our DSA-Net, we construct a contextual location module (CLM) as the spatial attention in the backbone stage and a global channel module (GCM) as the channel attention in the neck stage, respectively. The two separated attention modules enhance the discrimination between ship targets and complex inshore interferences. Moreover, a SkewCIoU loss considering both the angles and aspect ratios of ship targets is introduced to obtain a well-trained neural network with more accurate detection performance of slender ships. Experiments on the dataset of high-resolution ship collection 2016 (HRSC2016) manifest the superiority of the proposed algorithm in comparison to the existing state-of-the-art methods.

Fisher vectors (FVs) can capture multiple order information from superpixels (SPs) in synthetic aperture radar (SAR) images. Existing FV-based ship detectors mainly exploit the local contrast of FVs (LCFVs) but do not consider their global density features. This may lead to degraded performance in terms of discrimination between ship targets and the complex sea clutter. In this article, two new global cues from FVs are designed based on the fact that target FVs exhibit much lower densities than those of clutter FVs and also have large distances to the latter. Our two new global cues can suppress the sea clutter and significantly enhance ship targets throughout the SAR image. We also design an improved local cue from FVs for ship detection, in which the intensity contrast of SPs is incorporated into the existing LCFV indicator to reduce false alarms. By fusing the above two new global cues (and an improved local cue from FVs), we propose a new method for ship detection in SAR images. Experimental results based on Gaofen-3 SAR images show that the newly proposed detector provides better detection performance than other state-of-the-art detectors, especially in the presence of strong and highly heterogeneous sea clutter.

The simple linear iterative clustering (SLIC) has been shown as an efficient and widely used superpixel-based algorithm for segmenting marine synthetic aperture radar (SAR) images. However, SLIC does not consider the fact that the density of ship target pixels is significantly lower than that of sea clutter pixels, leading to a waste of computational cost and memory resources on lots of pure clutter areas and to the degradation of the compactness of superpixels. To address the aforementioned issues, we develop a new density-based SLIC (DSLIC) method for the superpixel-based segmentation of marine SAR images. In the initialization stage of our DSLIC, all the subimages in a large marine SAR image are rapidly prescreened via a new density-driven classifier, where most of the subimages only occupied by clutter pixels with comparatively high density are discarded and do not need to be segmented in the subsequent local clustering stage. The retained subimages contain both the clutter and potential target areas. This prescreening operation results in higher computation efficiency and memory savings. In the local clustering stage of DSLIC, besides the intensity proximity and the spatiality proximity (used in SLIC), the sparsity proximity (measured by density distances) is considered to reduce the coexistence of sparse target pixels with low density and nonsparse clutter pixels with high density within superpixels. Our theoretical and experimental results show that the proposed DSLIC method is faster and requires less memory than SLIC and other state-of-the-art superpixel-based segmentation methods for marine SAR images with similar or better segmentation accuracy.

In the paper, we propose a new method based on multi-order superpixel-level saliency and fuzzy logic (MSSFL) to fuse spaceborne and airborne SAR images for vessel detection. First, we generate a new global regional contrast map (GRCM) by exploiting the multi-order superpixel-level saliency (MSS). In the generated GRCM, the vessel targets are well restored and the backgrounds are suppressed. Next, a new fuzzy logic approach is presented to fuse the MSS information provided by the GRCMs. This GRCM-based fuzzy fusion can further enhance the vessel target regions and filter out the inshore interference regions. Experimental results using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR images show that the proposed MSSFL method yields higher target-to-cluster ratio (TCR) of fused images and improved detection performance compared with the commonly utilized image fusion approaches.

In this letter, we propose a new constant false alarm rate (CFAR) detector to accelerate the existing superpixel (SP)-based CFAR detectors for ship detection in synthetic aperture radar (SAR) images. In our method, we design a new density-censoring operation to rapidly identify background clutter SPs (BCSPs) with high densities before the local CFAR detection. In this way, a large number of non-informative BCSPs are removed without time-consuming calculation of decision thresholds, and only a few candidate ship target SPs (STSPs) are retained. This reduces the computational cost of the subsequent local CFAR detection and the number of false alarms produced by it. During the local CFAR detection process for the retained candidate STSPs, we also propose an improved method to define their neighboring clutter regions (for the calculation of decision thresholds) using BCSPs identified by the density-censoring operation. Experiments on measured SAR images validate that the proposed CFAR method reduces the computational cost of commonly used SP-based CFAR methods by 75%-96% with similar or better detection performance.

As a powerful coding strategy for superpixels in synthetic aperture radar (SAR) images, Fisher vector (FV) lies in a low-dimensional subspace and can be sparsely represented as a linear combination of training samples. The existing ship detection methods based on FVs often consider the Euclidean distances between target FVs and clutter FVs, where the subspace features of FVs are generally not exploited. In this article, we propose a new ship detection algorithm based on nonnegative sparse locality-representation (NSLR) to exploit the subspace features of FVs. The proposed NSLR method is based on the assumption that FVs of superpixels in SAR images are sparsely represented by the dictionary of background sea clutter only under a null hypothesis. In addition, we propose two FV-based filters to enhance the robustness of our newly developed NSLR to heterogeneous sea clutter environments by further exploiting the intrinsic features of ship targets in terms of intensity and spatiality. The experimental results based on Gaofen-3 SAR images demonstrate that the proposed NSLR detection method provides higher target-to-clutter contrast and achieves better detection performance than other commonly used ship detection algorithms.

In this paper, we propose an improved superpixel segmentation algorithm for ship target detection in synthetic aperture radar (SAR) images, called adaptive Fisher vector-based simple linear iterative clustering (AFVSLIC). Compared with existing algorithms, three new features produced by Fisher vectors, i.e., zero-order, first-order and second-order features, are exploited by the proposed AFVSLIC algorithm to enhance segmentation performance. Besides, AFVSLIC adaptively adjusts the weights of the features to maintain the segmentation performance in different signal-to-clutter ratio (SCR) scenarios. Experimental results demonstrate that the proposed AFVSLIC algorithm outperforms existing, commonly used algorithms for superpixel segmentation and (accordingly) improves the performance of ship target detection.

Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms.

In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.

We consider the distributed detection of weak signals from one-bit measurements collected by a sensor network where observation model uncertainties exist at all the sensor nodes. To solve this problem, a one-bit locally most powerful test (LMPT) detector is proposed in this letter. Moreover, asymptotically optimal one-bit quantizers at all the sensor nodes are designed for the proposed one-bit LMPT detector. In this letter, model uncertainties are interpreted as multiplicative noise and its variance represents the strength of model uncertainties. Theoretical analysis indicates that, when the strength of model uncertainties is finite, the proposed detector using one-bit data with πN/2 sensors approximately achieves the same detection performance as the clairvoyant detector that directly uses analog measurements with N sensors. Simulation results corroborate our theoretical analysis and show that, compared to the one-bit generalized likelihood ratio test detector, the proposed one-bit LMPT detector provides better detection performance in the presence of model uncertainties.

Conventional compressive sensing (CS) aims at sparse signal recovery from the measurements with continuous values. Quantized CS (QCS) methods arise in digital implementations where quantization of the receiver data is performed prior to signal processing. The extreme case of QCS is the so-called 1-bit CS where each real-valued measurement maintains only the sign information with one bit. The 1-bit CS alleviates the burden of storage and transmission of large data volumes and reduces the cost of the analog-to-digital converter. Recently, the 1-bit CS has been successfully applied to inverse scattering and radar imaging. In high-resolution radar imaging scenarios, targets assume spatial extent and occupy clustering pixels. The real and imaginary components of a complex sparse signal are the projections of the same complex value onto two orthogonal axes and, therefore, share a joint sparsity pattern. In this paper, a new 1-bit CS algorithm, referred to as enhanced-binary iterative hard thresholding (E-BIHT), is proposed to improve quality of 1-bit radar imaging by exploiting the two-level block sparsity exhibited in the two properties of clustering and the joint sparsity pattern of the real and imaginary parts of the target image. Simulations and experimental results demonstrate that compared to commonly used 1-bit CS algorithms, the proposed E-BIHT provides more informative imaging resulting in higher target-to-clutter ratio.

We consider the problem of detection of sparse stochastic signals with a distributed sensor network. Multiple sensors in the network are assumed to observe sparse signals, which share the joint sparsity pattern. The Bernoulli-Gaussian (BG) distribution with sparsity-enforcing capability is imposed on the sparse signals. The sparsity degree in the BG model is positive and close to zero in the presence of the sparse signals and is zero in the absence of the signals. Motivated by this, the problem of detection of the sparse signals with a distributed sensor network is formulated as the problem of close and one-sided hypothesis testing on the sparsity degree. For this problem, we propose a detector based on the locally most powerful test (LMPT) to decide on the presence or absence of sparse signals with sensor networks. The proposed LMPT detector does not require signal recovery, which alleviates the complexity of the detection system in sensor networks. Simulation results illustrate the performance of the proposed LMPT detector and corroborate our theoretical analysis. Simulation results also show that, compared to the detector based on matching pursuit, the proposed LMPT detector significantly reduces the computational burden without noticeable performance loss.

In this paper, we propose a two-level block matching pursuit (TLBMP) algorithm based on a probabilistic graph model for polarimetric through-wall radar imaging (TWRI). In typical L-band to X-band TWRI, indoor targets assume a spatial extent and occupy clustered pixels. When polarimetric sensing is used to obtain independent observations, radar images of clustered targets can be enhanced within the joint sparsity framework. Toward this objective, TLBMP is devised to exploit both the clustered property and the joint sparsity pattern of multiple polarimetric through-wall radar images. Simulations and experimental results based on polarimetric through-wall radar data demonstrate that compared to commonly used algorithms for solving the same underlying problem, TLBMP provides more informative imaging with higher target-to-clutter ratio.

This paper investigates the nonlinear dynamical features such as the bifurcation route of chaos and fractional structure in Superconducting Quantum Interference Device (SQUID). The dynamical equation of a superconducting quantum interference device composed of two Josephson junctions and a bias current source is derived, based on which we plot the bifurcation diagram and power spectral diagram of the considered superconducting circuits. We also show the bifurcation route to chaos and fractal behavior by calculating the box-counting dimension of such system. Our work opens up new dimension of research in various fields especially the nonlinear dynamics and its applications in quantum devices.

In this paper, we propose a novel greedy algorithm referred to as look-ahead hybrid matching pursuit (LAHMP) for multipolarization through-wall radar imaging (TWRI). From the viewpoint of compressive sensing, the task of multipolarization TWRI can be formulated as a problem of sparsity pattern recovery under the joint sparsity model. A newly developed greedy algorithm for joint sparsity model, hybrid matching pursuit (HMP), combines the strengths of orthogonal matching pursuit and subspace pursuit and improves the accuracy of the sparsity pattern recovery. Besides, the look-ahead strategy can select an optimal atom by evaluating its effectiveness on the overall reconstruction quality. Through integrating the virtues of HMP with the look-ahead strategy, the proposed LAHMP aims to more accurately select atoms corresponding to the true targets behind walls. Experiments based on measured radar data show that, compared to existing greedy algorithms, LAHMP provides better image quality at affordable expense of computational complexity.