Multi-drone multi-object tracking (MDMOT) aims to localize and identify targets from videos captured simultaneously by multiple drones. To accomplish this task, existing methods typically follow the strategy of associating localized targets to obtain identities. However, their localization and identification stages heavily rely on single-frame information, resulting in the localization being very sensitive to visual information decay and making it struggle to capture discriminative representations for target identification. Consequently, they usually exhibit unreliable performance in challenging scenarios, such as occlusion and high similarity among targets. To this end, we introduce a novel MDMOT framework to interact temporal-spatial features, exploring the guidance of tracklet information across time and space. Specifically, we introduce temporal-spatial feedback loops to enrich cues in our tracker. Meanwhile, a novel temporal-oriented target localization is proposed to enhance the response to difficult samples in feature space by utilizing prior knowledge from existing tracklets beyond the current frame for target localization. Moreover, a spatial-oriented target identification is designed to synergize cross-drone information of tracklets, thereby providing discriminative representations for target identification. It combines target and background information to extract identity representations and interacts features from multiple drones. To our best knowledge, this work reports the first MDMOT system that synergizes features across multiple drones to track targets. By incorporating these two elaborated networks, we develop a robust tracker (named TSMMT). Extensive experiments on the MDMT public dataset demonstrate the superiority of our proposed model. Specifically, TSMMT outperforms state-of-the-art methods by 2.76%~4.66% on MOTA and 2.06%~3.33% on IDF1.

Arbitrary-oriented ship detection in SAR imagery remains especially challenging due to multi-scale imbalance and the characteristics of SAR imaging, a problem that is more pronounced than in optical ship detection. Unlike optical images, SAR data often lack rich textural and color cues, instead exhibiting non-uniform scattering, speckle noise, and non-standard elliptical ship shapes, all of which make robust feature extraction and bounding box regression significantly more difficult across different scales. To address these unique SAR-specific challenges, this paper proposes the Multi-Scale Dynamic Feature Fusion Network (MSDFF-Net) aims to alleviate multi-scale imbalance in three main ways. First, a Multi-Scale Large-Kernel Convolution Block (MSLK-Block) integrates large-kernel convolutions with partitioned heterogeneous operations to enhance multi-scale feature representation, tackling wide-ranging ship sizes under noisy conditions. Second, a Dynamic Feature Fusion Block (DFF-Block) handles scale-based feature utilization imbalance by adaptively balancing spatial and channel information, thereby reducing interference from clutter and strengthening discrimination for diverse-scale ships. Third, we propose the Gaussian Probability Distribution (GPD) loss function, which models ships’ elliptical scattering properties and mitigates regression loss imbalance for targets of varying scales and orientations. Experimental evaluations on the R-SSDD, R-HRSID, and CEMEE datasets demonstrate that MSDFF-Net reaches top-tier performance standards, outperforming 21 existing deep learning-based SAR ship detectors. Specifically, MSDFF-Net achieves 93.95% precision, 94.72% recall, 91.55% mAP, 94.33% F1-Score, and 135.79 FPS on the R-SSDD dataset, with a parameter size of only 8.94 M. Additionally, MSDFF-Net exhibits strong transferability across large-scale SAR images, making it suitable for real-world deployment. The code and datasets can be accessed publicly at https://github.com/SZZ-SXM/MSDFF-Net.

Presents corrections to the paper, (Corrections to “Arbitrary-Direction SAR Ship Detection Method for Multiscale Imbalance”).

Geolocation is a crucial step in the processing of synthetic aperture radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travelling long distances through the troposphere. However, the impact of tropospheric delay on geolocation is often overlooked in existing airborne SAR studies, which can lead to inaccuracies. To address this issue, we propose a new positioning method, the tropospheric delay-compensated range-Doppler (TDC-RD) model. The TDC-RD model leverages reference atmospheric models to estimate and compensate for the tropospheric delay in SAR images. This model effectively mitigates the impact of tropospheric delay in SAR geolocation. To further optimize the TDC-RD model solution, a digital elevation model (DEM)-assisted dual iteration method is proposed. This method iteratively adjusts the target’s plane position and elevation in an alternating manner. The effectiveness of the TDC-RD model has been validated through both simulation experiments and actual flight experiments. The results show a significant improvement in geolocation accuracy compared to existing methods, with a maximum reduction of 11.39 m and 24.33% in the mean absolute error (MAE) of SAR geolocation. The TDC-RD model has a great advantage in long-range SAR geolocation. Our research enhances the accuracy and stability of high-altitude airborne SAR geolocation without requiring ground control points.

In practical SAR target detection applications, it is often encountered that the training and testing data come from different SAR sensors, leading to a decline in SAR target detection performance. Although domain adaptation methods can achieve model generalization through feature transfer, the change of scattering characteristics for the same target and the difference of feature distribution, caused by cross-sensor, cannot be ignored in SAR images. It is inevitable to lead to the escalation of the offset in the bounding box regression and deviation of the feature alignment. To address these issues, a cross-sensor SAR image target detection method based on dynamic feature discrimination and center-aware calibration is proposed. Based on the domain adaptation framework, initially, a Dynamic Feature Discrimination Module (DFDM) is introduced to address the exacerbated offset in the regression. A bidirectional spatial feature aggregation mechanism is employed to aggregate features in both horizontal and vertical directions and a multi-scale structure is adopted to enhance the scattering and semantic features, which can dynamically constrain the target position while improving target discrimination capability. Then, the Center-Aware Calibration Module (CACM) is designed to address the alignment deviation in feature transfer. The target salience relationship is modeled based on the distance between different positions and the target center to suppress background clutter interference. The perception center of the target is focused by combining the centerness map and classification map, which can calibrate the domain-invariant features and alleviate misalignment. Finally, the proposed method is tested on two datasets, MiniSAR and FARAD, and compared with the latest domain adaption methods. Both mAP and F1 values have improved by more than 6%-20%, verifying the effectiveness of the proposed method.

Deep learning (DL) techniques have recently ignited remarkable prosperity in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) field. Nevertheless, as targets of new categories are observed continually with few-shot examples in openly dynamic scenarios, endowing the DL-based SAR ATR systems with Few-Shot Class-Incremental Learning (FSCIL) ability is urgently demanded. In response, a Decoupled Scattering Augmentation Classifier (DSAC) is proposed to mitigate both intrinsic and domain-specific challenges of the FSCIL of SAR ATR. Specifically, as the significant partability of target structures in SAR imagery, virtual targets with potential scattering patterns are synthesized and pre-allocated by a Scattering Augmentation Module (SAM) to unleash the model’s forward compatibility for future categories. Once deployed, the DSAC is decoupled with dynamic worlds for prompt knowledge representation. Also, a prototypical Nearest-Class-Mean (NCM) classifier with cosine criterion is leveraged for stable and general identification. Extensive experiments conducted on an FSCIL of SAR ATR dataset verify the superiority of our method compared to various latest benchmarks.

Synthetic aperture radar (SAR) automatic target recognition (ATR) has attracted extensive attention and achieved satisfactory results. However, most SAR ATR methods follow the closed-set assumption, which assumes that all target classes in the test set have been contained by the training set. In actual scenarios, it may encounter the target classes that are not included in the training set, and it presents a challenge for SAR ATR. To tackle this issue, this letter proposes an open-set recognition method based on joint training of class-specific sub-dictionary learning. First, joint training is used to optimize the sub-dictionary learning process, and it could significantly enhance the discriminative ability of these sub-dictionaries. Second, the reconstruction errors of the targets on each sub-dictionary are calculated. These errors can be split into matched and no-matched errors. Third, extreme value theory (EVT) is employed to model the matched and no-matched errors of each class, which could determine the class boundaries. Finally, for the target to be recognized, its reconstruction errors on each sub-dictionary are calculated individually. The class of this target can be determined by comparing the errors with these class boundaries. Our method achieved an accuracy of 87.22–94.02 and an F1 score of 88.03–90.25 in multiple experiments on moving and stationary target automatic recognition (MSTAR) dataset. Compared with several state-of-the-art methods, it has better accuracy and robustness.

Ship recognition in synthetic aperture radar (SAR) is an essential challenge in SAR image interpretation. The measured SAR ship targets often contain complex background such as port facilities and neighboring ships, which are easy to interfere with the model and affect the recognition performance. To address this issue, a SAR ship recognition method with complex background based on dual-branch transformer fusion network is proposed in this paper. First of all, a dual-branch feature extraction and fusion architecture is designed in this paper, including significant feature extraction (SFE), global feature extraction (GFE), and dual-branch feature fusion (D-BFF). Specifically, the SFE effectively extracts the most discriminative local fine-grained features of ship target using multi-layer convolution of significant regions. The GFE capture global semantic information by residual module optimization. In addition, combined with the self-attention in the transformer block based on cross-attention and position encoding, the effective fusion of SFE and GFE is realized in D-BFF. Finally, extensive experiments are carried out based on Gaofen-3 seven-category dataset (anyone can get the dataset after sending the applying e-mail). The results reveal that the proposed method can achieve a recognition accuracy of 75.55%, which is significantly superior to other algorithms.

Timely monitoring of ships is imperative for ensuring the safety and security of maritime operations. Ship detection in synthetic aperture radar (SAR) is typically applicable to focused images. The time consumption of target detection primarily relies on the imaging process duration, encompassing intricate and time-intensive processing steps such as range migration correction and azimuth compression. Consequently, achieving real-time SAR ship detection poses a significant challenge. To address these issues, ship detection in the range-compressed domain of SAR has emerged as a viable approach. However, there is still a lack of reliable ship detection datasets that can satisfy the detection on the range-compressed domain. In this paper, we construct a dataset specifically designed for ship detection in range-compressed SAR data, called RCShip-1.0 (range-compressed ship dataset). The original data source is publicly available complex-valued data from the Sentinel-1 acquisition and the OpenSARShip-1.0 dataset, encompassing numerous ship targets. Subsequently, the inverse chirp scaling (ICS) algorithm is employed on the complex-valued data to acquire range-compressed SAR data. RCShip-1.0 encompasses training set, validation set, and test set acquired through two distinct approaches. It consists of 1580 large-scale SAR range-compressed images which are further divided into 18322 sub-images to facilitate subsequent display and analysis of detection results within large-scale SAR images. The experimental results demonstrate that each deep network achieves good performance on the dataset, with an F1-score exceeding 65%. The utilization of the RCShip-1.0 dataset in obtaining these experimental outcomes showcases its feasibility, standardization, and public availability.

The data distribution of synthetic aperture radar (SAR) vehicle targets in the actual missions is often imbalanced. However, the recent algorithms for SAR target recognition are designed either under abundant samples, or the situation of few labeled samples among all the categories. These cases all avoid facing the difficulties of imbalanced data distribution,i.e. the difference between the number of labeled samples among categories is huge. The samples in the majority classes will get more chances to be learnt by the deep neural network, which impedes the regular algorithms from achieving a high recognition rate. In this letter, a design guideline for imbalance loss and an example of data distribution (DD) loss based on the guideline is proposed, which provides an extremely effective way of handling the problem of imbalanced SAR target recognition. The DD loss takes the sample distribution and the data quantity of SAR vehicle targets into consideration. It can cause images with fewer samples in their categories to decrease more gradients proportionally. Moreover, the proposed DD loss adds no more burden to the networks and compared to other imbalanced algorithms with complex processes, the DD loss can be conducted easily. Plenty of experiments, which involve two various kinds of imbalanced datasets, are implemented and the proposed DD loss shows excellent performance among these imbalanced datasets. When there are only 40 labeled samples in minority categories, the DD loss can achieve over 95% in nine different cases, which exceeds other methods and losses of at least 7%.

Cross-view geo-localization aims to associate geographical location with different view images shot from different platforms. One of the critical challenges is how to effectively emphasize architectural features and reducing background interference to achieve robust cross-view matching. Most of the existing methods fail to adequately address the features of buildings, treating foreground and background equally. Leveraging prior knowledge to enhance the features of crucial architectural foreground yields greater benefits in Geo-Localization. A comprehensive three stage approach (TirSA) is proposed in this paper, which consists of three components: Pre-processing, Generate Feature Embedding, and Post-processing. In the Pre-processing stage, we employ a self-supervised feature enhancement method (SFEM) to obtain the building aware mask. Without adding additional auxiliary information, the model is guided to learn from discriminative building regions. Besides, in the Generate Feature Embedding stage, we propose an adaptive feature integration module (AFIM) to enhance feature representation capability. We also train the Siamese network using a novel improved cross-domain triplet loss to reduce the impact of inter-view domain gap. Finally, in the Post-processing stage, we employ a re-ranking method to optimize the initial retrieval list, further enhancing the matching accuracy. Remarkably, extensive experiments show that our proposed TirSA exceeds state-of-the-art by a large margin and achieves optimality in both drone-view target localization and drone navigation. Especially in the drone navigation task, our method is superior to the existing methods, achieving an improvement of approximately 5%. Code will be released at https://github.com/SunJ1025/TirSA.

Fine-grained aircraft detection from synthetic aperture radar (SAR) imagery is of significance in transportation and military domains. Based on the prior knowledge that aircraft are frequently found in airports, current research adopts airport-to-aircraft detection pipelines for aircraft detection and classification in panoramic SAR images. Geospatial information, including the approximate airport location and the spatial relationships between the airport and aircraft, represents valuable supplementary information that can enhance fine-grained aircraft detection performance. However, due to unreliable geographical information and the limited perceptual field, it is challenging to detect and classify aircraft in panoramic SAR imagery. To address this, a novel geospatial contextual prior-enabled knowledge reasoning framework is proposed. First, an unsupervised and lightweight geospatial-driven airport detection (AD) method is presented by combining geographic information matching and optical-to-SAR image registration, which can quickly locate airports and narrow the scope for fine detection. Then, an improved real-time model for object detection with a recursive-gated spatial interaction module (RTMDet-RSIM) is proposed for fine-grained aircraft detection. RTMDet-RSIM uses frequency-domain analysis to improve global information modeling ability without excessive computational cost. Finally, a relational prior-based reasoning strategy is proposed by modeling the geospatial category relationships from historical images to strengthen classification performance. Experiments on 61 panoramic SAR images covering 13 aircraft categories show that the proposed method achieves high detection accuracy with a mean average precision (mAP) of 81.2%, while the average test time for an image size of$8738\times 7636$is about 8.58 s. The source code and dataset will be released.

With the rapid acquisition of high-resolution Synthetic Aperture Radar(SAR) images, new categories are continually observed with few-shot instances in openly non-cooperative scenarios. Powering a SAR Automatic Target Recognition (SAR ATR) system with an ability of few-shot class-incremental learning (FSCIL) is nontrivial. Observing the pronounced azimuth-dependence and part-sparsity of targets in SAR images, an Azimuth-aware Subspace Classifier (AASC) on the Grassmannian manifold is proposed to tackle the FSCIL of SAR ATR stably and accurately. In the AASC, losses covering both semantic and manifold facets, which include Semantic Margin Separation (SMS), Deep Subspace Separation (DSS), and Structure Less Forgetting (SLF), are designed to strike both the intrinsic model’s stability and plasticity dilemma and domain-specific challenges. For plasticity, the novel-to-old semantic margins are enlarged by the SMS loss for knowledge transferring while avoiding inappropriate adaptions. The DSS loss derived from the Grassmannian geometry aims to regularize class subspaces orthogonality. For stability, semantic drifts of target spatial and global structures are punished by the SLF loss. As the periodicity and volatility of target azimuth-aware patterns, an Azimuth-aware Exemplar Selection (AES) strategy is designed to select representative and complementary exemplars. In experiments, the advantages of the subspace classifier and the designed losses and strategies are deeply verified. Comprehensive experiments on three FSCIL scenarios derived from both airborne and spaceborne datasets, including the MSTAR, the SAR-AIRcraft-1.0, and self-collected data sets, show that our method significantly outperforms various task-specific benchmarks, verifying its effectiveness for the FSCIL in real SAR ATR scenarios.

Detecting ships in synthetic aperture radar (SAR) images poses a formidable challenge, primarily attributed to limited observation samples and complex environments. To address this problem, driven by latent similarity between optical and SAR images, we propose a domain-adaptive few-shot detection algorithm for SAR ship detection [single shot multibox detector (SSD)]. The algorithm requires only a few training samples of SAR images and effectively combines them with rich optical images to utilize domain information. First, we develop an efficient plug-and-play distance metric function. This function accurately measures the distances between features from the optical domain and the SAR domain. Second, we design a lossy branching mechanism to effectively utilize SAR domain knowledge. This branching mechanism is driven by the observed latent similarity in domain knowledge distribution between optical and SAR images. In addition, we introduce a dual-stream branching feature alignment extraction network with weight sharing. This network architecture enables better knowledge extraction and sharing between optical and SAR domains. To evaluate our method, we conducted experiments on a newly created dataset, DIOR2SSDD, which is designed for few-shot SAR image ship detections across optical and SAR domains. The experimental results show that under three-, five-, and ten-shot settings, the mean average precision (mAP) of our method can reach 59.2%, 61.2%, and 64.6%, and with only 10% SAR training data, the mAP can reach 89.3%. It indicates that our method can effectively transfer domain knowledge and achieve excellent ship detection performance in SAR images.

In response to the problems existing in traditional SAR image target detection methods, such as complicated processes, long detection times, and poor detection effects in complex backgrounds, this paper proposes an integration of detection and recognition method based on deep learning for large-scene SAR images with vehicle targets. The paper introduces the issues of target identification through three stages of target detection, identification, and classification in traditional methods. To address these problems, this paper introduces a one-stage detection network based on YOLOv5 to construct a SAR image vehicle target detection and recognition algorithm. To verify the performance of the algorithm, this paper generated a dataset containing 10 different vehicle targets in large-scene SAR images and applied it to experiments. The results demonstrate that the algorithm has good performance and fast detection speed. The research results of this paper can provide important references for large-scale SAR image target detection.

Machine learning methods for synthetic aperture radar (SAR) image automatic target recognition (ATR) can be divided into two main types: traditional methods and deep learning methods. The deep learning methods can learn the high-dimensional features of the target directly, and usually obtain high target recognition accuracy. However, they lack full consideration of SAR targets’ inherent characteristics resulting in poor generalization and interpretation ability. Compared with the deep learning methods, traditional methods can get more interpretable and stable results with model-based features. In order to take full advantage of these two kinds of methods, we propose target part attention network based on the attributed scattering center (ASC) model to integrate the electromagnetic characteristics with the deep learning framework. Firstly, considering the importance of scattering structure for SAR ATR, we design a target part model based on ASC model. Then, a novel part attention module based on Scaled Dot-Product Attention mechanism is proposed, which directly associates the features of target parts with the classification results. Finally, we give the derivation method of the importance of each part, which is of great significance for practical application and the interpretation of SAR ATR. Experiments on the MSTAR data set demonstrate the effectiveness of the proposed part attention network. Compared with existing studies, it can achieve higher and more robust classification accuracy under different complex conditions. Furthermore, combined with the importance of parts, we constructed two effective interpretable analysis methods for deep learning network classification results.

In the context of ship monitoring in the ocean, targets are usually sparsely distributed. Thus, synthetic aperture radar (SAR) imaging of the whole scene is usually quite redundant and costly. However, raw SAR echo data were considered to be useless before focusing. Few studies have attempted to detect ships from raw SAR echo data. It seems to be an impossible task since the resolution of raw SAR echo data is too low. This article proposes a ship detection method for raw SAR echo data in view of a nonimaging target sensing paradigm. The core idea is that we can sense the existence of ships from raw SAR echo data without imaging. The underlying rationale is that the radar always speaks the same sentence, i.e., usually an exactly identical linear frequency modulated (LFM) signal, while target and clutter answer differently. The difference spread into each part of the whole echo sequence rather than only the focused energy after match filtering. Thus, the ships can be found by pattern analysis on one-dimension sequence data rather than two-dimension images. The experimental results based on simulation and typical real data validate our assumption. This study shows that SAR imaging is an unnecessary intermediate process and opens up new significant possibilities for ship detection in the vast ocean.

SAR target classification is an important application in SAR image interpretation. In practical applications, the battlefield is open and dynamic, and the SAR target classification model often encounters the targets of unknown classes. However, most of the existing SAR target classification methods follow the close-set assumption. It makes them only classify several fixed classes of targets and can’t deal with the targets from unknown classes. To this end, this paper proposes a novel SAR target classification method. This method can not only classify the targets from known classes and search targets from unknown classes but also incrementally update the classification model with these unknown class targets. Specifically, an autoencoder improved by MS-SSIM (multi-scale structural similarity) loss is utilized to extract targets’ features, and it can better utilize the structural information in SAR images. Next, the classifier based on EVT (Extreme Value Theorem) is established, which can classify the known class targets and search the unknown class targets. Then, we perform improved model reduction on the established classifier. This operation could speed up the model and prepare for incremental learning. Finally, after manually labeling those unknown class targets, the classifier is updated with these data in incremental form. Experimental results on the MSTAR (Moving and Stationary Target Automatic Recognition) dataset indicate that, compared with the state-of-the-art methods, our proposed method has better performance in open set recognition and incremental learning.

Most deep-learning based synthetic aperture radar (SAR) target classification methods directly apply models designed for natural scenes and do not consider the difference between SAR and optical images. To solve this problem, a parts model guided multi-level fusion network for synthetic aperture radar (SAR) target classification is proposed in this paper, which integrates the electromagnetic scattering features into the deep learning network. Firstly, attribute scattering center (ASC) model based target parts (ASC-Parts) are extracted, which provide scattering features of target from physical model. Then, under the guidance of the ASC-Parts model, the local features of target are further extracted based on the proposed SAR target parts segmentation network. Through this physics guided network, we can introduce the target scattering characteristics into deep learning. Finally, a multi-level fusion structure is developed, which adds the local features to the global features obtained from different layers of traditional deep learning network. The experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) data set show the superiority of the novel method and illustrate the effectiveness of physics guided deep learning for SAR target classification.

Most of synthetic aperture radar (SAR) based ship detection methods utilize two-dimension focused images. Ship detection in range-compressed data is promising since it needs no time-consuming azimuth focusing. This paper proposes a ship detection method in range-compressed SAR data, which employs the statistical characteristics and range trajectory of a ship target in the range-compressed time-domain. First, it employs complex signal kurtosis (CSK) to prescreen potential ship areas since CSK was demonstrated to be an reasonable indicator for SAR ship detection. Then, a convolutional neural networks (CNN) based discrimination is applied to the potential ship areas. The training samples comes from the simulation results of range trajectory based on the radar imaging parameters. Preliminary results show that the proposed method performs well in range-compressed SAR data.

Target region segmentation of synthetic aperture radar (SAR) images is one of the challenging problems in SAR image interpretation. The existing conventional segmentation methods rely on parameter selection in different backgrounds. Compared with traditional methods, the deep-learning-based methods can reduce the dependency on parameters and achieve more accurate results. However, lacking annotation data limits the application of the deep-learning-based methods in SAR chip image segmentation aspect. To solve these problems, a refined network structure for SAR vehicle image semantic segmentation, namely, All-Convolutional networks (A-ConvNets)-based Mask (ACM) net, is proposed. The mask in the training dataset of the network is extracted from image reconstruction using the Attribute Scattering Center (ASC) model, which can solve the problem of the lack of manual annotation in the segmentation methods based on deep learning. The proposed ACM Net consists of a modified A-ConvNets-based backbone and two decoupled head branches which achieve target segmentation and label prediction results, respectively. Experiments on moving and stationary target acquisition and recognition (MSTAR) dataset show that the comprehensive segmentation performance of ACM Net is better than both traditional segmentation methods and deep-learning-based segmentation methods. The classification results outperform other instance or semantic segmentation methods with the state-of-the-art recognition accuracy.

Synthetic aperture radar (SAR) images are highly susceptible to imaging conditions. However, the majority of deep learning (DL) models in SAR automatic target recognition (ATR) adopt enhanced network structures similar to those in dealing with optical image classification tasks, which is obviously unreasonable since the huge gap of the imaging conditions between training and testing data severely deteriorates the recognition performance. The main idea of the framework is to introduce SAR imaging condition information into the DL training stage to eliminate domain discrepancies between training and testing data. Based on this framework, we propose a task-driven domain adaptation (TDDA) transfer learning method, which can alleviate the degradation of recognition caused by the variance of depression angle between training and testing data. In order to introduce the prior imaging information into the method, simulated SAR data is first obtained by adding a simulated object radar reflectivity to a terrain model of individual point scatters using the known training and testing SAR imaging parameters. Then a domain confusion metric and a supervised classification loss are calculated on simulated data and source training data, respectively, to learn a representation that is semantically meaningful and domain invariant. Comparative experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed method can obtain better recognition performance than the other methods.

Most of the existing synthetic aperture radar (SAR) automatic target recognition (ATR) methods aim at the closed set situation, in which the classes of targets in the test set have appeared in the training set. However, in practice, the classifier is likely to encounter the targets from unseen categories and classify them incorrectly, which brings a huge challenge to current SAR ATR techniques. To overcome this problem, this letter proposes an open set recognition (OSR) method based on multitask learning, and the method is developed from generative adversarial network (GAN). Essentially, this method decomposes OSR into two tasks: classification and abnormal detection. The classification task is the same as that in the closed set situation, while the abnormal detection task is used to determine whether the targets belongs to the unseen categories. Correspondingly, the network structure of GAN is modified and the other full-connection network branch is added to the end of the discriminator, so it has the ability to accomplish the above two tasks. Finally, according to the results of two tasks, the OSR for SAR targets can be realized. The experimental results on moving and stationary target acquisition (MSTAR) dataset demonstrate that the proposed method has the better recall, precision,$F1$, and accuracy than other OSR methods.

Deep learning has been widely used in automatic target recognition (ATR) for synthetic aperture radar (SAR) recently. However, most of the studies are based on the network structure in optical images and lack full consideration of the inherent characteristics of SAR targets, which limits the improvement of recognition accuracy and makes poor generalization ability. In addition, due to the black-box characteristics, it is difficult to effectively interpret SAR ATR results. To conquer these problems, we propose an electromagnetic scattering feature (ESF) module embedded network based on attributed scattering center (ASC) model to incorporate the SAR targets’ characteristics into the deep learning framework. First, a novel convolutional neural network (CNN)-based algorithm for extracting ASC parameters is proposed, which makes the network focus on target features under the guidance of physical model. Then, the ESF module is designed based on a well-trained ASC parameters extractor to inject the learned target features into the classification network for more robust and interpretable results. Besides, two structures are proposed combined with the ESF module for single-view and multiview SAR target classification, which further illustrates the portability of the module. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show the validity of the proposed CNN-based ASC extractor and the ESF module embedded classification network. Compared with ordinary networks, our method can achieve higher classification accuracy under complex conditions, which reflects the better generalization performance of the algorithm. Furthermore, through visualization analysis of the classification results, we show the interpretability of the network combined with the electromagnetic scattering characteristics.

Synthetic aperture radar (SAR) signatures of ship targets are often degraded by various types of distortions due to the distinctive imaging mechanism. The negative impacts could affect the characterization and identification of ships. Recently, complex signal kurtosis (CSK) was found to be a vital indicator of ship detection in SAR images. Since CSK can be used as an indicator of ship detection, we consider it can also be used to indicate ship target signature. One of the basic rationales is that the larger the CSK is, the easier it is to be detected and thus the more obvious its characteristics are as a ship. Defocusing and sidelobes are two common problems that affect the quality of ships. Their impacts on ship target signature are studied and compared from the perspective of CSK. Specifically, a ship target signature improvement methodology based on the maximum CSK criterion is proposed for both refocusing and sidelobe suppression. The role the CSK plays in these improvements and the underlying rationales are elaborated. In addition, a preliminary evaluation of the global imaging quality is also provided. Experimental results based on real data demonstrate that CSK can be used to indicate and improve ship target signature.

Synthetic aperture radar (SAR) target recognition faces the challenge that there are very little labeled data. Although few-shot learning methods are developed to extract more information from a small amount of labeled data to avoid overfitting problems, recent few-shot or limited-data SAR target recognition algorithms overlook the unique SAR imaging mechanism. Domain knowledge-powered two-stream deep network (DKTS-N) is proposed in this study, which incorporates SAR domain knowledge related to the azimuth angle, the amplitude, and the phase data of vehicles, making it a pioneering work in few-shot SAR vehicle recognition. The two-stream deep network, extracting the features of the entire image and image patches, is proposed for more effective use of the SAR domain knowledge. To measure the structural information distance between the global and local features of vehicles, the deep Earth mover’s distance is improved to cope with the features from a two-stream deep network. Considering the sensitivity of the azimuth angle in SAR vehicle recognition, the nearest neighbor classifier replaces the structured fully connected layer for$K$-shot classification. All experiments are conducted under the configuration that the SARSIM and the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset work as a source and target task, respectively. Our proposed DKTS-N achieved 49.26% and 96.15% under ten-way one-shot and ten-way 25-shot, whose labeled samples are randomly selected from the training set. In standard operating condition (SOC) as well as three extended operating conditions (EOCs), DKTS-N demonstrated overwhelming advantages in accuracy and time consumption compared with other few-shot learning methods in$K$-shot recognition tasks.

Synthetic Aperture Radar (SAR) is an important means for ship surveillance at sea due to its unique advantages of all-day and all-weather. This paper proposes a ship target detection method in SAR image based on Google Earth Engine remote sensing cloud computing platform. It can realize the real-time and fast detection of ship targets in a large area, the acquisition of ship target information and the batch download of SAR images in the detection area. Compared with the traditional ship target detection methods in SAR images, the proposed method is not limited to the acquisition, storage, utilization and batch processing of SAR satellite remote sensing data, and does not have high requirements on computer performance. Therefore, it has high practical application value for marine ship surveillance.

Adversarial training is an effective method to enhance adversarial robustness for deep neural networks. However, it qequires large amounts of labeled data, which are often difficult to acquire. Recent research has shown that self-supervised learning can help to improve model performance and model uncertainty using unlabeled data. In this paper, we introduce a new adversarial self-supervised learning framework to learn a robust pretrained model for remote sensing scene classification. The proposed method exploits the advantage of dual network structure, and it requires neither labeled data for adversarial example generation nor negative samples for contrastive learning. Specifically, it consists of three major steps. Firstly, we train the online model and the target model to extract deep image features. Secondly, we generate two kinds of instance-wise adversarial examples. Finally, we iteratively learn a robust model by implicit comparing the difference between clean data and their perturbed counterpart. Preliminary experimental results on remote sensing scene classification dataset shows that our method can obtain higher robust accuracy. Our method can also be combined with other adversarial defense techniques to further promote model robustness.

The C-band Sentinel-1 synthetic aperture radar (SAR) images are affected by radio frequency interference (RFI), and this article proposes an RFI detection and localization method for these images. First, a detailed analysis of RFI based on the generation mechanism is provided, which shows that RFI in the ground range detected (GRD) products differs from common backscattering not only in frequency spectrums but also in radiometric characteristics. Then, an RFI index (RFII) is proposed for RFI detection, which takes full advantage of unique RFI characteristics in dual-polarization GRD images. Finally, both detection results in ascending and descending passes are used to locate the ground RFI sources, assuming that the presence of RFI is persistent. Theoretical analyses show that the location area is a diamond of approximately 88.76 km2. Experimental results demonstrate that the proposed method performs quite well on Sentinel-1 GRD data. It provides a convenient and tractable tool for assessing Sentinel-1 data quality as well as for monitoring RFI. Thus, Sentinel-1 measurements can be used to monitor C-band RFI, which is an important task of electromagnetic spectrum management.

Water segmentation is a fundamental step for the information processing of SAR image, which plays an important role in ship detection, disaster monitoring and other applications. Because of the complexity of scenario in the SAR images, water segmentation of SAR image is a challenging task. Fewer convolutional neural networks (CNNs) have been developed for SAR image water segmentation in recent years, the accuracy and speed of CNN for water segmentation can be further improved. In this paper, we established a SAR water segmentation dataset based on the GF3 satellite data. then an improved water segmentation network based on Bilateral Segmentation Network (BiSeNet) is proposed. Further we propose a loss function based on edge area and a novel training data generation method to improve the segmentation ability of the network. Experimental results based on water segmentation dataset show that the proposed segmentation method has better segmentation accuracy and speed.

Recently, complex signal kurtosis (CSK) is found to be a vital indicator of ship detection in synthetic aperture radar (SAR) images. Since CSK can be used as an indicator of ship detection, we consider it can also be used to indicate ship target signature. One of the basic rationales is that the higher the CSK is, the easier it is to be detected and thus the more obvious its characteristics are as a ship. Presence of sidelobes and defocusing are two common problems that affect the quality of ship targets. They are discussed in this paper from the perspective of CSK. Experimental results show that CSK can be used to indicate and improve ship quality. We believe that ship detection and recognition can benefit from the CSK indicator.

Complex generalized Gaussian distribution (CGGD) is quite significant in synthetic aperture radar (SAR) modeling since original focused SAR data are complex-valued. However, the estimation method of the vital parameter of the CGGD, i.e., the shape parameter, is seldom studied. This letter proposes a fast shape parameter estimation method of the CGGD in SAR images. The proposed method is developed based on a concept in the complex signal processing field, i.e., complex signal kurtosis (CSK). Specifically, this letter provides an introduction to the CSK at first. Then, the relationship between the shape parameter and the CSK is elaborated. Finally, the estimation chain based on the relationship is proposed. Experimental results demonstrate that the proposed method outperforms the state-the-of-art, i.e., the maximum-likelihood (ML) method proposed by Novey et al. It works in a near-real-time fashion with good estimation precision, being much faster than Novey's method and achieving better performance in distinguishing different kinds of non-Gaussianity of typical SAR targets.

Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need sea-land segmentation before detection, and inaccurate sea-land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top K values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using F1 as matric and executes 14% faster.

Recent studies have shown that complex information in single-channel synthetic aperture radar (SAR) imagery has practically always been underrated. This improves the perception of their potential for ocean monitoring. Based on the in-depth interpretation of complex signal kurtosis (CSK), this paper proposes a new ship detection method based on CSK in single-channel SAR imagery. The proposed method consists of two main parts, i.e., region proposal and target identification. The basic idea is to first detect potential ship locations based on the region proposal. Then, the final ship target is acquired based on the target identification. Compared to conventional methods based on detected products, e.g., the constant false alarm rate (CFAR), the proposed method has three advantages. First, CSK can take advantage of both non-Gaussianity and noncircularity, which is the fundamental concept distinguishing complex signal analysis from the real case. Second, the proposed method can be intrinsically free of false alarms caused by radio frequency interference (RFI). Finally, the proposed method can avoid missing detection in dense target situations. This methodology has been demonstrated over significant data sets acquired from Sentinel-1, TerraSAR-X, and Gaofen-3. These results validate that CSK is a vital indicator of ship detection. Complex information is expected to play a more important role in single-channel SAR imagery.

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.

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 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%.

In this paper, we address the problem of semi-supervised visual domain adaptation for transferring scene category models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a multiple kernel learning domain adaptation algorithm to fuse the information from multiple features and cope with the considerable variation in feature distributions between images from two domains. For each image, we first extract eight state-of-art local features and use the pretrained scene attribute model from ground-level SUN attribute database to predict attribute labels. For each scene class we learn an adapted target classifier based on multiple feature kernels by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Experimental results demonstrate that it is possible to use a scene category model learned on a set of ground view scenes for semi-supervised classification of VHR remote sensing images.

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.

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.

In image processing, the detection of keypoint plays a significant role in foundation work of many image applications. Most widely distributed features in images are corners. Among the famous corner detectors, Harris detector has shown its excellent performance. However, when coming up with SAR image, the speckle noise may severely influence the performance of Harris. And Harris is seldom used in SAR image processing. In this paper, we take advantage of the premium properties of the Bilateral Filter which is robust to speckle noise while preserving the details. Then we propose a new corner detector called bf-Harris. We study the performance of the proposed detector and compare it to several existing approaches. The result shows the algorithm has an excellent performance.

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.

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.

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.

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.

This paper focuses on the problem of moving human target detection in foliage environment, which is a challenge in a radar system. As a matter of fact, owing to the rough surfaces of trunks, branches, and leaves, there is always a lot of multipath clutter remaining which will severely influence the detection performance. In the study, a method combined with entropy weighted coherent integration (EWCI) and the Hough transform is put forward. The method can effectively suppress not only the stationary clutter but also the multipath clutter. The nonline-of-sight (NLOS) foliage-penetration measurements are set up and the results prove the superiority of our method.

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.

The scattering measurements of individual pixels in polarimetric SAR images are affected by speckle; hence, the performance of classification approaches, taking individual pixels as elements, would be damaged. By introducing the spatial relation between adjacent pixels, a novel classification method, taking regions as elements, is proposed using a Markov random field (MRF). In this method, an image is oversegmented into a large amount of rectangular regions first. Then, to use fully the statisticalaprioriknowledge of the data and the spatial relation of neighboring pixels, a Wishart MRF model, combining the Wishart distribution with the MRF, is proposed, and an iterative conditional mode algorithm is adopted to adjust oversegmentation results so that the shapes of all regions match the ground truth better. Finally, a Wishart-based maximum likelihood, based on regions, is used to obtain a classification map. Real polarimetric images are used in experiments. Compared with the other three frequently used methods, higher accuracy is observed, and classification maps are in better agreement with the initial ground maps, using the proposed method.

This paper presents a technique for the extraction of roads in a high resolution synthetic aperture radar (SAR) image using Hough transform. Roads in a high resolution SAR image can be modeled as a homogeneous dark area bounded by two parallel boundaries. Dark areas, which represent the candidate positions for roads, are extracted from the image using a Gaussian probability iteration segmentation, and the roads are accurately detected by Hough transform. For this purpose, we designed an average Hough transform, which is more reasonable than general Hough transform for the extraction of lines. We search the peak values in Hough space and try to reduce its overall computational cost by introducing a global CFAR detector. In this process, to detect roads more accurately, post-processing, including noisy dark regions removal and false roads removal, is performed. We applied our method to MSTAR clutter images of Redstone that have a resolution of about 1 ft /spl times/ 1 ft. The experimental results show that our method can accurately detect roads.