Remote sensing instance segmentation aims to detect and assign pixel-level labels to each instance in remote sensing images, which holds critical engineering significance for both civil and military applications. While existing domain-specific methods have made progress, they still struggle with three persistent challenges: ineffective context modeling in cluttered backgrounds, information loss during multi-scale feature fusion, and blurred boundaries for densely clustered small objects. To address these limitations, we propose a novel remote sensing instance segmentation framework with three artificial intelligence (AI) methodological innovations, which comprises: a Context Perception Module (CPM) for context modeling, a Context Guided Multi-Scale Feature Aggregation (CGFA) method for multi-scale feature fusion, and a Multi-Path Region Proposal Extractor (MPRPE) with boundary-refined segmentation. The CPM leverages the selective state space model (Mamba) to capture long-range contextual information, effectively addressing the issue of cluttered backgrounds in remote sensing images. The CGFA replaces standard feature pyramid network architecture which is limited by direct summation or concatenation, preserving fine-grained spatial details with context guidance. The MPRPE and boundary-aware segmentation head mitigate the challenges of missed detection of small objects and blurred edge predictions, which arise from the clustered distribution of small objects and semantic ambiguity. Extensive experiments on the challenging iSAID and NWPU VHR-10 datasets validate the proposed method’s consistent improvements across metrics while demonstrating its practical engineering impact on remote sensing interpretation systems.

• Research highlight 1: We propose a novel cross-task hybrid training methodology for multimodal images, offering a simple yet unified solution that simultaneously addresses both image generation and fusion tasks. • Research highlight 2: Building upon mutual-supervised multimodal image pairs, we innovatively integrate single-modality self-supervision to develop a hybrid-supervised decoupling framework with a dedicated loss function, achieving robust separation of content-style representations. • Research highlight 3: Extensive experiments spanning on four modalities and seven popular datasets demonstrate our method’s consistent superiority and impressive cross-task capability. Ablation studies further reveal that our framework learns generalized representations transferable across different image processing tasks. Multimodal image fusion and cross-modal translation are fundamental yet challenging tasks in computer vision, with their performance directly impacting downstream applications. Existing approaches typically treat these tasks independently, developing specialized models that fail to exploit the intrinsic relationships between different modalities. This limitation not only restricts model generalizability but also hinders further performance improvements. In this paper, we propose a joint optimization framework for image generation and fusion. Specifically, we generalize multimodal image tasks as the fusion and transformation of cross-modal features, and design a hybrid task training strategy. At the data level, we introduce a self-supervised and mutual-supervised hybrid mechanism for content-style feature decoupling, which achieves superior feature separation through stepwise training on intra-modal and cross-modal data. At the model level, we construct a triple-branch decoupling head along with fusion and transformation modules to ensure synchronous and efficient execution of dual tasks. Our method not only breaks through the single task limitation of the model, but also innovatively introduces mixed supervision into multimodal processing. We conduct comprehensive experiments covering four modalities fusion tasks on seven popular datasets. Extensive experimental results demonstrate that our method achieves superior performance on two tasks as compared of the respective state-of-the-art methods, and show impressive cross-task generalization capability.

Unmanned aerial vehicles (UAV) are widely used for their small size and flexibility. However, the large number of small objects and the significant difference in object size in UAV images bring great challenges to the detection task. Therefore, we propose an object detection method for UAV images with four improvements on the strong baseline model YOLOX-S, which is robust to detect small objects and multi-scale objects. Firstly, we introduce a high-resolution feature map to retain rich detailed information about small objects. Secondly, we propose new up-sampling and down-sampling modules to reduce the feature information loss during the sampling process. Thirdly, we present the triple-scale feature fusion module (TSFFM) to fuse more abundant multi-scale features in the neck’s bottom-up feature fusion process. Finally, the parrell dilated convolution attention module (PD-CAM) is proposed to learn the multi-receptive field features. Experiment results on the VisDrone-VID2019 dataset validate the effectiveness and superiority of the proposed method.

Pointly Supervised Object Detection (PSOD) has attracted considerable interests due to its lower labeling cost as compared to box-level supervised object detection. However, the complex scenes, densely packed and dynamic-scale objects in Remote Sensing (RS) images hinder the development of PSOD methods in RS field. In this paper, we make the first attempt to achieve RS object detection with single point supervision, and propose a PSOD method tailored for RS images. Specifically, we design a point label upgrader (PLUG) to generate pseudo box labels from single point labels, and then use the pseudo boxes to supervise the optimization of existing detectors. Moreover, to handle the challenge of the densely packed objects in RS images, we propose a sparse feature guided semantic prediction module which can generate high-quality semantic maps by fully exploiting informative cues from sparse objects. Extensive ablation studies on the DOTA dataset have validated the effectiveness of our method. Our method can achieve significantly better performance as compared to state-of-the-art image-level and point-level supervised detection methods, and reduce the performance gap between PSOD and box-level supervised object detection. Code is available at https://github.com/heshitian/PLUG.

Supervised learning based object detectors suffer from the high cost and difficulty of labeling datasets. Self-supervised learning methods require no manual annotations. However, the misalignment between the pretext task designed for image classification and the downstream task affects the detection performance. Therefore, this paper proposes a self-supervised dense contrastive learning method to improve performance of object detection in remote sensing images. Specifically, first, Swin Transformer substitutes popular CNN to extract features of augmented multiple views. Second, global and local features are extracted using parallel global and dense projector heads, respectively. Third, a predictor head is added to increase the nonlinear transformations in the network. Extensive experiments on the NWPU VHR-10 dataset show that the proposed method outperforms two representative strong baseline methods, including MoCoV2 and DenseCL.

Semantic segmentation of high-resolution optical remote sensing images is an important but challenging task. To solve the problem that many semantic segmentation networks fail to efficiently utilize global and local context information to improve the segmentation performance, this paper proposes a semantic segmentation network based on sparse self-attention (SDANet) to model the global context dependencies. Specifically, the feature maps are first divided into four regions in spatial and channel dimensions, respectively, and the divided feature maps are rearranged to form new regions. Second, the position and channel self-attention operations are performed on the rearranged regions. Third, the feature maps are restored to the original combination and the position together with channel self-attention operations are performed again to obtain the output feature maps. Finally, semantic segmentation is completed based on the output feature maps. Extensive experiments conducted on the ISPRS Vaihingen dataset demonstrate that the proposed method is superior to self-attention-based DANet, CCNet, and other general semantic segmentation networks, such as FCN, Deeplabv3+, HRNet, etc.

The translation of synthetic aperture radar (SAR) to optical images provides a new solution for the interpretation of SAR images. Most of the existing translation networks are based on generative adversarial networks and use 9-residual blocks or U-Net structures in the feature inference phase. Both structures cause a large amount of information lost during the conversion of SAR image features to optical features, making the outline of the translated image blurred or semantic information lost. Aiming at this problem, this paper proposes a cross-fusion inference network structure, which preserves both high-resolution features and low-resolution features in the whole process of feature inference. Our proposed method broadens the network horizontally while deepening it vertically and improving the image translation performance. The experiments conducted on the public dataset sen1-2 show that the proposed method is superior to other networks.

To enhance mid–low-resolution ship detection, existing methods generally use image super-resolution (SR) as a preprocessing step and feed the super-resolved images to the detectors. However, these methods only use high-resolution (HR) images as ground-truth labels to supervise the training of their SR module but overlook the rich HR information in the detection stage. Inspired by the recent advances in knowledge distillation, in this letter, we design a feature distillation framework to fully exploit the information in ground-truth HR images to handle mid–low-resolution ship detection. Our framework consists of a student network and a teacher network. The student network first super-resolves input images using an SR module and then feeds the super-resolved images to the detection module. The teacher network whose architecture is the same as the student detection module directly takes HR images as input to generate HR feature representation and then distills these HR features to the student network through a distillation loss. Using our feature distillation framework, HR images are not only used as ground-truth labels to train the SR module but also provide “ground-truth” features to train the detection module, which enhances the detection performance of the student network. We apply our framework to several popular detectors, includingFCOS,Faster-RCNN,Mask-RCNN, andCascase-RCNN, and conduct extensive ablation studies to validate its effectiveness and generality. Experimental results on the HRSC2016, DOTA, and NWPU VHR-10 datasets demonstrate that, when applying our framework toFaster-RCNN, our method can outperform several state-of-the-art detection methods in terms of mAP50 and mAP75.

Automatic aircraft target recognition, including direction detection and fine-grained classification, is an important but challenging problem. Multi-directional densely arranged targets and the tiny differences between classes cause difficulties in recognition and direction prediction. To overcome the aforementioned problems, a rotated hybrid task cascade (RHTC) network is proposed. Specifically, RHTC cascades the segmentation branch and the bounding-box (bbox) branch to fuse the semantic feature in a coarse- to- fine manner. In addition, a new oriented bounding box regressor (OBBR) is proposed to predict the direction of target, and a new directionalloss function is added to further optimize the regressor. Moreover, we design fine masks in preprocessing to achieve improved recognition performance. The experimental results evaluated on the datasets collected from Google Earth show that RHTC can achieve the state-of-the-art performance on self-defined direction precision (DP) and mean average precision (mAP).

High-resolution remote sensing images can provide abundant appearance information for ship detection. Although several existing methods use image super-resolution (SR) approaches to improve the detection performance, they consider image SR and ship detection as two separate processes and overlook the internal coherence between these two correlated tasks. In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet. In our method, we not only feed the super-resolved images to the detector but also integrate the intermediate features of the SR network with those of the detection network. In this way, the informative feature representation extracted by the SR network can be fully used for ship detection. Experimental results on the HRSC dataset validate the effectiveness of our method. Our ShipSRDet can recover the missing details from the input image and achieves promising ship detection performance.

The distance measure plays a crucial role in the PolSAR image superpixel segmentation. In most cases, the commonly used simple weighting is adopted to combine multiple distance measures to calculate the similarity, thus leading to large computational burden and low segmentation performance. To solve this problem, this paper proposes a novel PolSAR image superpixel segmentation method based on a novel cross iteration strategy to incorporate the advantages of the geodesic distance and the revised Wishart distance. First, the PolSAR image is initialized as hexagonal distribution and all pixels are set as unstable pixels. Second, the revised Wishart distance and geodesic distance are adopted by the cross iteration strategy to relabel all unstable pixels. Finally, the postprocessing procedure is used to generate the final superpixels. Extensive experiments conducted on the AirSAR dataset demonstrate that the proposed method exhibits higher computational efficiency and more regular shape, resulting in smooth representation of the land covers in homogeneous regions, and better preserved details in heterogeneous regions.

Recently, convolutional neural networks (CNNs) have been successfully developed and used in the classification of polarimetric synthetic aperture radar (PolSAR) images. However, they often suffer from some problems, such as time-consuming, unsatisfactory detail-preservation, and bad effectiveness given limited training samples. Focusing on these problems, we propose a complex-valued CNN (CV-CNN)-based algorithm for PolSAR image classification in this article. On the one hand, a superpixel-oriented (SPO) scheme is employed to reduce the computational cost of the algorithm and preserve image details simultaneously, which takes superpixels instead of single pixels as classification units. In particular, to meet the input requirement of CV-CNN, three alternative methods of superpixel regularization are designed and compared. On the other hand, considering that both measured data (MD) and manually designed polarimetric features (PFs) have their own advantages, the hybrid data (HD) combining them is employed to drive CV-CNN, which is helpful to improve the effectiveness of the algorithm. We perform experiments on three actual PolSAR image data sets acquired by AIRSAR and Radarsat-2 systems as well as a semisimulated data set. The experimental results demonstrate that, compared to conventional pixel-oriented methods, the proposed SPO scheme is much more time-efficient and is also beneficial to detail preservation. Moreover, the CV-CNN driven by HD generally obtains consistently better classification results than that driven by pure MD or manually designed PFs.

Recently, many deep convolutional neural networks (CNNs) have been developed for the polarimetric synthetic aperture radar (PolSAR) image classification. For them, it is often a hard task to obtain good results with limited training samples. To release this problem, some strategies, such as the feature-driven method that takes artificial features as the input of CNN, have been proposed. However, since artificial features are usually difficult to be universal, their applicability is limited to a certain extent. For this issue, this paper proposes a scheme of combining measured data and artificial features with a complex-valued CNN (CV-CNN). In our algorithm, not only the measured PolSAR data but also some discriminative artificial features are employed as the input of CV-CNN. The basic idea is that the measured data contains the fully acquired information of targets, while the artificial features include expert knowledge. Therefore, by fusing them, better and more stable performance may be obtained. The experiments performed on both actual and simulated PolSAR images have validated the effectiveness of the proposed algorithm.

Automatic inshore ship recognition, including target localization and type classification, is an important and challenging problem. However, arbitrarily rotated ships are always moored inshore densely. This makes it very difficult to locate ship targets. To resolve this problem, we proposes a multiclass oriented ship localization and recognition framework based on a cascade region convolutional neural network (R-CNN). First, Cascade R-CNN is adopted to localize and classify the positive regions - a set of bounding boxes (BBox). Second, a novel procedure which transforms a bounding box to a rotated bounding box (B2RB) is designed and applied to each BBox to regress a rotated BBox (RBox) and non-maximum suppression (NMS) is adopted to remove redundant RBoxes. Extensive experimental results conducted on the dataset collected from Google Earth demonstrate the effectiveness of our proposed approach, compared to two other state-of-the-art approaches.

Complex information in single-channel synthetic aperture radar (SAR) imagery is seldom used. This is a common practice based on the conventional resolution theory. However, with the advent of high-resolution SAR sensors, information in the complex data has been found to be of significance for ocean applications. In particular, we note that there is a special type of instrumental artifact in Sentinel-1 images. It is rarely researched and may be attributed to radio frequency interference (RFI). It has similar intensity with ships and can degrade ocean interpretation performance severely. This paper proposes an innovative method to discriminate ships from RFIs based on noncircularity and non-Gaussianity. Among them, noncircularity is calculated based on the measure called normalized noncircularity, and non-Gaussianity is estimated based on the complex generalized Gaussian distribution. The discrimination rationale is analyzed in detail. The experimental procedure is based on Sentinel-1 interferometric wide swath products. Only cross-polarization data are tested since RFIs are quite weak in co-polarization data. It is found that noncircularity and non-Gaussianity can characterize and identify the difference between ships and RFIs. Ships present larger noncircularity and sup-Gaussianity while RFIs are found to exhibit quite low noncircularity and mainly show sub-Gaussianity. The proposed method achieves quite good performance. These results show that noncircularity and non-Gaussianity are extremely helpful complements for single-channel SAR imagery interpretation.

Due to the rapid evolution of satellite systems, traditional nearest neighbor image retrieval methods used in large-scale image retrieval usually cause “curse of dimensionality” that leads to boosting feature storage and slow retrieval speed. The hashing method, which aims at mapping the high-dimensional data to compact binary hash codes in Hamming space and quickly calculates the Hamming distance by bit operation and XOR operation, can effectively achieve search and retrieval with remaining similarity for big data. In this paper, we propose a novel image retrieval method based on deep hashing learning, called deep semantic hashing(DSH), attempting to mining the semantic information of remote sensing(RS) images. Experiments carried out on an archive of RS images point out that DSH outperforms other methods to achieve the state-of-the-art performance in image retrieval applications.

Region-based edge detectors are popular for edge extraction of polarimetric synthetic aperture radar (PolSAR) images, which, however, often suffer from the heterogeneous data and outliers. In this paper, an improved edge detector with a scheme of refining samples automatically is proposed. Firstly, dominant scattering mechanisms of PolSAR data are acquired by using the Freeman-Durden decomposition. Then, for each pixel, samples in the regions predicted by edge detector filter are refined according to their dominant scattering mechanisms and power. Furthermore, for filters of different orientations, statistical distances between the refined samples in two regions are calculated, and the maximum is assigned to be the corresponding edge intensity. The experiments performed on both simulated and actual PolSAR images demonstrate that the proposed approach is more robust to outliers than the classical algorithms.

This paper proposes a superpixel-based unsupervised classification framework for polarimetric synthetic aperture radar (PolSAR) images. First, the PolSAR image is over-segmented into a variety of superpixels, based on which mean Freeman decomposition and HSI color feature vectors are extracted and stacked directly into a high-dimensional feature vector. Second, based on a distance matrix constructing from the high-dimensional feature vector, visual assessment of tendency using diagonal tracking (VATdt) is adopted to adaptively estimate the number of terrain classes and automatically capture the cluster structure. Third, spectral clustering and a reduction technique as well as complex Wishart classifier are performed to obtain the final classification results. Experiments conducted on one simulated and one real-world PolSAR images demonstrate the superiority and effectiveness of the proposed method.

Fast multiclass object detection for remote sensing images plays an important role for a wide range of applications. Traditional methods based on a sliding window search lead to heavy computational costs and are unsuitable for multiclass detection. Recently, deep learning algorithms, especially faster region based convolutional neural networks (Faster R-CNN), which adopt a region proposal paradigm to avoid exhaustive search, has achieved state-of-the-art multiclass detection performance in computer vision. This paper investigates the use of Faster R-CNN in the earth observation community. We have three contributions: 1) It's the first time to successfully use Faster R-CNN for object detection in remote sensing images. It achieved faster speed (22 ×faster) and better performance (a mAP of 78% vs. 72%) than traditional methods; 2) we adopt data augmentation to train Faster R-CNN with limited samples; 3) we successfully tested our method on large-scale google earth images, which shows robustness of our method.

This paper proposes a ship detection method based on weighted support vector machines (SVM) and m-χ decomposition in compact polarimetric (CP) synthetic aperture radar (SAR) imagery. Firstly, the proposed method constructs the weighted feature vectors by extracting CP parameters. Each feature will be weighted by the ReliefF method. Then, ship targets in CP SAR imagery are detected by the weighted SVM classifier. Finally, false alarms are removed by scattering mechanism strength differences corresponding to three components of m-χ decomposition. NASA/JPL AIRSAR airborne quad-polarimetric (QP) data are used to simulate the CP data in the circular transmitlinear receive (CTLR) mode. Experimental results show that the method performs well in detecting ship targets, and can reject azimuth ambiguities.

Traditionally, phase content and information contained in the complex data in single-channel synthetic aperture radar (SAR) imagery is often discarded based on the conventional resolution theory. With the rapid development of SAR technology, however, ship target is no longer a point target but an extended target in high resolution SAR imagery. Thus, the conventional resolution theory is not strictly applicable to high resolution SAR imagery. Noncircularity can describe the distribution consistency between the real and imaginary parts. In this paper, we proposed a method using noncircularity parameters for ship detection in high resolution SAR imagery. The potential by using noncircularity parameters for ship detection is studied in detail. Experimental results based on TerraSAR-X data show that noncircularity parameters can identify ship targets well and can discriminate azimuth ambiguities. We believe that noncircularity parameters can benefit ship detection in various research aspects.

This paper proposes a coupled region based convolutional neural networks (R-CNN) to automatically detect vehicles in aerial images. Traditional methods are mostly based on sliding-window search, and use handcrafted or shallow-learning based features. They have limited description ability and heavy computational costs. Recently, a series of R-CNN based methods have achieved great success in general object detection. Inspired by the previous work, we propose a coupled R-CNN to detect small size vehicles in large-scale aerial images. First, a vehicle proposal network (VPN) is proposed to generate candidate vehicle-like regions, using a hyper feature map combined by feature maps of different layers. Then, a vehicle classification network (VCN) is developed to further verify the candidate regions and classify vehicles in eight directions. In this study, our method is tested on a challenge Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and speed compared to existing methods.

This paper proposes a PolSAR imagery unsupervised classification framework based on consensus similarity network fusion (CSNF), which is generally utilized for biomedical Sciences and for the first time used for PolSAR imagery classification in our work. First, the PolSAR image is divided into superpixels by a fast superpixel segmentation method and five groups of feature vectors are extracted based on the superpixels. Second, CSNF is performed on the five affinity matrixes constructed from the five groups of feature vectors to obtain a fused similarity matrix. Third, spectral clustering based on the fused similarity matrix is adopted to automatically achieve the classification results. Finally, a postprocessing procedure based on dissimilarity measure is performed to smooth the classification results and correct the misclassified pixels. The experimental results conducted on both a simulated PolSAR image and a real-world PolSAR image show the superiority of the proposed method.

The deep convolutional neural network (CNN) has been widely used for target classification, because it can learn highly useful representations from data. However, it is difficult to apply a CNN for synthetic aperture radar (SAR) target classification directly, for it often requires a large volume of labeled training data, which is impractical for SAR applications. The highway network is a newly proposed architecture based on CNN that can be trained with smaller data sets. This letter proposes a novel architecture called the convolutional highway unit to train deeper networks with limited SAR data. The unit architecture is formed by modified convolutional highway layers, a maxpool layer, and a dropout layer. Then, the networks can be flexibly formed by stacking the unit architecture to extract deep feature representations for classification. Experimental results on the moving and stationary target acquisition and recognition data set indicate that the branched ensemble model based on the unit architecture can achieve 99% classification accuracy with all training data. When the training data are reduced to 30%, the classification accuracy of the ensemble model can still reach 94.97%.

Land masking is one of the most important stages for ship detection in synthetic aperture radar (SAR) images. However, a fast and efficient algorithm for land masking in SAR images is far from resolved. Current land masking algorithms are time-consuming or not accurate enough for ship detection in SAR images. In this paper, an algorithm for land masking is proposed. It is designed for ship detection in SAR images based on a series of image processing steps. Experimental results based on real SAR data demonstrate that the algorithm proposed in this paper is fast and accurate enough for ship detection in SAR images.

To reduce the impact of outliers and noises on point pattern matching, a novel point pattern matching algorithm based on local topological characteristic and probabilistic relaxation labeling (LTC-PRL) is proposed in this paper. For each point in a point set, partial adjacent points are used to describe its local topological characteristic. To avoid the defects in angle coding of the existing global topological characteristic, a binary adjacent code is adopted in the local topological characteristic. And since the assignment of angle is greater than the distance, bigger weight is given to the angle while computing the similarity of the local topological characteristic among points. Finally, a robust compatibility measurement is defined and the support function is iterated by probabilistic relaxation labeling to get the best matching result. Experiments on synthetic data and the real image data show that the LTC-PRL has great matching performance when outliers and noises exist.

With the rapid development of spaceborne Synthetic Aperture Radar (SAR) and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of very great importance. Focusing on practical problems of adaptive ship detection, this paper present a highly adaptive ship detection method for spaceborne SAR imagery. It applies two different detection strategies to high and low resolution SAR imagery respectively. By taking into account the imaging mode, incidence angle, polarization channel of SAR imagery, it implements the adaptive ship detection in spaceborne SAR imagery. Experimental results based on real data show that the proposed method is able to detect all ship targets adaptively in a real-time fashion.

Speckle existed in SAR image is an undesirable product of specific imaging principle which influences SAR image interpretation and processing. In this paper, a new SAR image denoising algorithm has been proposed combining cluster with sparse representation under the non-local methodology. Due to the similar clustered patches, the sparsity coding of clustered patches is sparser. And clustered patches with similar structure could have the same constraint condition defined by the center of clustering. Thus, the non-local patches are clustered and filtered as a whole with shrinked sparsity coding. This algorithm has preferable denoising results on both simulated images and real SAR images. Experiments show prospects with speckle of different degrees compared with state-of-the-art despeckling methods. Proposed algorithm performs well both in noise reduction and detail preservation.

The simple linear iterative clustering (SLIC) method is a popular recently proposed superpixel algorithm. However, it may provide bad superpixels for the synthetic aperture radar (SAR) images due to the influence of speckle and large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed by employing the probability density function (PDF) information of SAR image pixel clusters. In this algorithm, a local clustering scheme combining data similarity with spatial proximity is designed, instead of the local k-means clustering used in the standard SLIC method. Moreover, for the post-processing, an edge evolving scheme with a local Bayesian criterion is introduced, instead of the connected components algorithm. In addition, for the precise statistical modeling of SAR images, the generalized gamma distribution (G?D) is exploited. Finally, the superiority of the proposed algorithm is validated on both simulated and real-world SAR images.

The authors address the problem of coherent multitarget localisation for distributed multiple‐input multiple‐output (MIMO) radar, in the presence of phase synchronisation mismatch between each transmitter‐receiver pair. The inherent sparsity of targets in the surveillance area can be exploited to represent radar data and then target locations are accurately estimated using sparse reconstruction. However, due to the difficulty of perfect phase synchronisation, the localisation technique is usually required to eliminate the phase errors. This study jointly considers the phase error correction problem in the context of multitarget localisation. In this novel method, the direct position determination of multitarget is obtained by estimating the spare reflection coefficients and phase errors alternately. Numerical simulation results demonstrate that the authors’ iterative block sparse Bayesian learning via maximum likelihood estimation algorithm obtains enhanced estimation accuracy against the phase synchronisation mismatch.

Bilateral filtering is a technique to smooth images while preserving edges; it employs both geometric closeness and intensity similarity of neighbouring pixels. When intensity similarity of neighbouring pixels is very high, however, bilateral filtering weakens into Gaussian filtering. The performance does not improve significantly while the computation is still expensive. Many existing accelerated algorithms, however, ignored this basic fact. In this study, a hybrid bilateral filtering algorithm based on edge detection is proposed. By making use of edge detection, the proposed algorithm combines bilateral filtering and Gaussian filtering and its degree can be controlled by a threshold. Experimental results show that the proposed algorithm is able to reduce the computation efficiently and achieve better performance. What is more, the proposed algorithm shows potential to speed up existing accelerated bilateral filtering algorithms.

We address the problem of unsupervised visual domain adaptation for transferring scene category models and scene attribute models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a discriminative cross-view subspace alignment algorithm where each view is represented by a subspace spanned by eigenvectors. The source subspace is created using partial least squares correlation, whereas the target subspace is constructed by principal component analysis. Then, a mapping that aligns the source subspace and the target subspace is learned by minimizing a Bregman matrix divergence function. Finally, we project the labeled source data into the target aligned source subspace and the unlabeled target data into the target subspace and perform classification. Experimental results demonstrate that it is possible to use a scene category model or a scene attribute model learned on a set of ground view scenes for classification of VHR remote sensing images. Furthermore, the transferred visual attribute-based representations are human understandable and the classification results are better or comparable with state-of-the-art methods.

A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

For the classification of synthetic aperture radar (SAR) images, traditional pixel-based Bayesian classifiers suffer from an intrinsic flaw that categories with serious overlapped probability density functions cannot be well classified. To solve this problem, in this letter, a region-based classifier for SAR images is proposed, where regions, instead of individual pixels, are treated as elements for classification. In the algorithm, each region is assigned to the class that minimizes a criterion referring to the Kullback-Leibler distance. Besides, the generalized gamma distribution (GΓD), a flexible empirical model, is employed for the statistical modeling of SAR images. Finally, with a synthetic image and an actual SAR image acquired by the EMISAR system, the effectiveness of the proposed algorithm is validated, compared with the pixel-based maximum-likelihood method and two region-based Bayesian classifiers.

This letter proposes a novel segmentation algorithm for synthetic aperture radar (SAR) images based on hierarchical region merging and edge evolving. To cope with the influence of speckle in SAR images, a statistical stepwise criterion, the loss of log-likelihood function (LLF) of image partition, is utilized for region merging. For this merging procedure, precise distributions of image partitions are essential, and we employ the generalized gamma distribution (GΓD) for modeling SAR images. Besides, the traditional region merging methods often suffer from the initial image partition that may lead to coarse segment shapes. It motivates us introducing a novel edge evolving scheme into the segmentation algorithm. It consists of two iterative steps: the evolution of edge pixels with a maximum likelihood (ML) criterion and that with a maximum a posterior (MAP) criterion using a Markov random field (MRF) model. The performance of the proposed algorithm is validated on two actual SAR images from the AIRSAR and EMISAR systems.

Anomaly detection (AD) has increasingly become important in hyperspectral imagery (HSI) owing to its high spatial and spectral resolutions. Many anomaly detectors have been proposed, and most of them are based on a Reed–Xiaoli (RX) detector, which assumes that the spectrum signature of HSI pixels can be modeled with Gaussian distributions. However, recent studies show that the Gaussian and other unimodal distributions are not a good fit to the data and often lead to many false alarms. This letter proposes a novel hyperspectral AD algorithm based on local sparsity divergence (LSD) without any distribution hypothesis. Our algorithm exploits the fact that targets and background lie in different low-dimensional subspaces and that targets cannot be effectively represented by their local surrounding background. A sliding dual-window strategy is first adopted to construct local spectral and spatial dictionaries, which enable the extraction of the sparse coefficients of each HSI pixel. Then, a consistent sparsity divergence index is proposed to compute the LSD map at each spectral band separately. Finally, joint segmentation of LSD maps over different bands is performed for AD. Experimental results on both simulated data and recorded data demonstrate the effectiveness of the proposed algorithm.

Ship classification is an important step in maritime surveillance utilizing synthetic aperture radar images. In this paper, we focus on the classifier architecture. The paper investigates three individual classifiers, i.e., the K nearest neighbor classifier, the Bayes classifier, and the back-propagation neural network classifier from the viewpoint of discrimination measurements firstly. Then, we propose a SVM combination strategy to fuse the results of individual classifiers. Extensive experiments conducted on the TerraSAR-X SAR images validate the effectiveness of the proposed method.

In this letter, a novel constant false alarm rate (CFAR) detection algorithm for high-resolution synthetic aperture radar (SAR) images is proposed with the generalized gamma distribution (GΓD) modeling the background. At first, the method of log-cumulants is introduced for estimating the parameters of the GΓD. In addition, a closed-form expression for the detection threshold of the CFAR algorithm is derived, which refers to the inverse incomplete gamma function. Finally, comparing with the algorithms using the Weibull,KA, andGA0distributions for background, the advantages of the proposed algorithm, including maintaining the false alarm rate and the efficiency, are validated with an actual high-resolution SAR image.

Ship classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new ship classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation classification (SRC) method is exploited. In particular, to describe the ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).

We present a novel fusion algorithm for electronic-reconnaissance (ER) satellite and optical imaging satellite data using coherent point set (CPS) analysis. This work is motivated by a large-scale maritime surveillance problem, where ship groups in the observations are of particular interest for tactical and strategic operations. Fusion of observations from ER satellite and optical imaging satellite is a challenging task. On the one hand, dense and continuous measurement is not available for optical imagery. On the other hand, it is difficult to extract robust features from ER measurements. Considering that the size of a ship is often less than the distance among different ships, we treat each ship as a mass point. The contributions of our work are threefold. First, multisensor data fusion is accomplished by CPS association. To the best of our knowledge, this letter is the first to investigate CPS for multimodal remote sensing data fusion. Second, a novel geometry descriptor, which encodes the topological characteristics of a point set, is presented. Third, we combine both topological features and attributive features within the framework of Dempster–Shafer theory for CPS analysis. The proposed method has been tested using different sets of simulated data and recorded data. Experimental results demonstrate the effectiveness of the proposed method.

High resolution SAR imagery captures both the sea background and ship target more explicitly. This paper proposed an algorithm based on EXS-C-CFAR (excision-switching context based CFAR) and Alpha-stable distribution to detect ships in high resolution SAR imagery. From experiment results, it is derived that the Alpha-stable distribution models spiky sea clutter well and the EXS-C-CFAR has good ship detection performance on JPL/NASA AIRSAR data. Moreover, context information utilized in the detector preserves more ship structures.