Training of synthetic aperture radar (SAR) target detection and recognition methods based on deep learning heavily relies on a large amount of data. As one of the significant approaches to address the scarcity of SAR data, SAR image intelligent generation methods have witnessed rapid development. However, these methods often require many data samples for learning and are prone to deviating from the physical scattering characteristics. To address these issues, this paper proposes a two-stage SAR image generation method based on attribute feature decoupling within a generative adversarial network (GAN) architecture. In the first stage, the original SAR target image undergoes feature extraction and reconstruction, yielding generated images highly similar to real images. The attribute features decoupled during this process correlate with the scattering characteristics of SAR target, providing guiding information for generating target images in the second stage. In the second stage, by applying perturbations to specific dimensions of the decoupled features, we can reconstruct target images with altered attributes, achieving diverse data augmentation. Multi-task discrimination based on pixel intensity, authenticity, and feature distance differences enhances the quality of generated images across multiple levels. The decoupled representation-driven generation paradigm simplifies the network’s mapping learning task through task decomposition, diminishing the dependency on the volume of data. The experimental results demonstrate that the generated images possess higher quality and superior application performance, with an improvement of 5.23% in recognition accuracy.

Synthetic aperture radar (SAR) is an effective imaging and observation sensor that has been widely applied in both military and civilian fields. Deep learning approaches have gained prominence in SAR target recognition and received extensive attention. However, these methods often struggle when data is scarce, leading to insufficient training and challenges in effective feature extraction. To address this limitation, we propose a less data-dependent feature extraction framework. Specifically, we introduce the dual-tree complex wavelet transform (DTCWT) to capture multi-frequency feature details of SAR images, integrated with convolutional neural network. This approach enables effective extraction of high- and low-frequency information. By leveraging the characteristics of these frequency features, low-frequency subbands are used to emphasize the global structural features in the images, and high-frequency subbands are employed to identify the significance of different regions in the images. In response to the aforementioned characteristics, we introduced an attention mechanism to effectively incorporate high-frequency local information into low-frequency global information, thereby enhancing feature representation and recognition efficiency. Moreover, we propose an adaptive rotational convolution, and apply it to the high-frequency feature extraction. The adaptive rotational convolution can adapt to the directionally selective subbands with a single convolution kernel. Experiments conducted on the MSTAR and SAR car datasets demonstrate that the proposed method can achieve better recognition performance with fewer parameters, especially on small-scale datasets. The ablation study also confirms the effectiveness of the introduced DTCWT and rotational convolution.

Existing SAR ship detectors perform well on data with consistent distributions but degrade significantly when faced with domain shifts and the absence of labeled data. Moreover, traditional CNNs struggle with global feature extraction due to the local receptive fields while transformer approaches struggle with computational efficiency when extracting global features from complex SAR images. Designing an effective cross-domain SAR ship detector that can handle unlabeled data with domain shifts remains a challenge. In this letter, we propose a novel Mamba-based unsupervised domain adaptation SAR ship detection model integrated with pseudo labels optimization strategy. First, we propose the Domain Adaptive State Space Model (DASSM) to construct the Mamba Mean Teacher framework for the first time, enhancing the capture of both global and local SAR image features at a linear time complexity and facilitating domain-invariant feature learning. To enhance the quality of pseudo labels, we design the Adaptive Pseudo Label Optimizer (APLO) module with Wise-IoU (WIoU) and dynamic dual-threshold pseudo label selector (DDPLS). The WIoU is utilized to improve the generation of pseudo labels, while DDPLS is further employed to categorize and optimize pseudo labels. Extensive experiments on public datasets illustrate the effectiveness and superiority of the proposed method for cross-domain detection of unlabeled SAR data.

Man-made targets subjected to different polarized waves will produce different depolarization effects, and these differences contain abundant information beneficial for recognition. However, traditional manually designed features struggle to fully utilize polarimetric information for scattering characterization. This letter proposes a target scattering characteristic learning network based on the Null-Pol response, which adaptively extracts the proportions of typical scattering mechanisms from mixed scattering mechanisms. Firstly, by leveraging polarimetric modulation, the Discrete Null-Pol Synthesis Pattern (DNSP) is designed to fully reveal the differences in target scattering mechanisms. On this basis, we propose an end-to-end scattering inversion network module to learn the DNSPs of different typical targets under scattering ambiguity conditions, obtaining polarimetric scattering contribution of 10 typical structures. Finally, we conduct structure recognition experiments to demonstrate the effectiveness of the proposed module. The results show that the proposed method can effectively characterize scattering behavior and significantly improve the performance of target structure recognition.

Aircraft detection in synthetic aperture radar (SAR) images is one challenging task due to the discreteness of aircraft scattering, the diversity of aircraft size, and the interference of background. In order to deal with these problems, a novel method named scattering enhancement and feature fusion network (SEFFNet) is here proposed to detect aircraft via combining traditional image processing and deep learning together. At first, a scattering information extraction and enhancement module (SIEEM) is proposed to highlight the scattering points of aircraft targets. Then, to more effectively focus on the location of aircraft targets, a space-to-depth coordinate attention module (SDCAM) is further designed, following which an efficient multi-scale feature fusion pyramid (FFP) is also introduced to fuse the semantic information of different layers. At last, a contextual fusion head (CFH) is built to improve the receptive field for better detecting aircraft. The experiments carried out on the popular datasets SADD and SAR-AIRcraft-1.0 show that SEFFNet is more appropriate for aircraft detection, especially the small-size aircraft detection, in comparison with other state-of-the-art (SOTA) methods. Taking the dataset SADD for example, on average, the precision, recall, F1-score, and APs values are respectively 2.8%, 2.6%, 2.7%, and 2.0% higher than the baseline network YOLOv5.

Vehicle detection is an important application of polarimetric synthetic aperture radar (PolSAR). Geometrical perturbation polarimetric notch filter (GP-PNF) establishes a feature space based on the local background polarimetric characteristics to achieve adaptive detection of ship targets. However, the complexity of the ground background presents additional challenges compared to sea surface. In this work we model the distribution of the GP-PNF and prove its effectiveness and accuracy compared with other common distribution models based on real airborne mini-SAR data. And then we introduce a numerical calculation of logarithm cumulants for parameters estimation, derive the constant false alarm rate (CFAR) threshold computation formula and apply the filter to vehicle detection. Experiments performed on real high-resolution PolSAR images verify the good performance of the detection method.

Large-scale, diverse and high-quality data is the foundation and key to achieving good generalization in target detection and recognition for deep learning-based algorithms. Directly collecting synthetic aperture radar (SAR) image data faces the difficulty in acquisition and high costs. Traditional SAR image simulation methods are limited by geometric and electromagnetic computation errors in their modeling process, and the high computational burden as well. Generative adversarial networks (GANs) offer a new approach for SAR image generation, but they struggle to achieve satisfactory results in terms of image quality and diversity. In order to overcome this problem, we propose a new type of GAN to learn the spatial relationship of the targets more effectively. Taking the real SAR images as input, we extract the target information through the capsule network, perturb the extracted features and adopt the attention mechanism to improve the quality and diversity of the augmented data.

Deep learning methods have made profound advancements in the field of synthetic aperture radar (SAR) target recognition. Typically, a significant amount of training data is required. However, due to the high degree of prior expert knowledge required for the annotation of SAR images, it is challenging to obtain a large amount of labeled data, which significantly impacts the performance of target recognition. To address this issue, this paper introduces an Azimuth-Aware Multi-Head Residual Self-Attention Network (MHRA-Net) that can extract high discriminative features of targets. Initially, this model employs a sub-aperture decomposition method to expand target information across multiple azimuth angles. Subsequently, we design a multi-head residual self-attention mechanism that can extract salient features of targets from multiple perspectives. Finally, a multi-scale feature fusion module is used to extract both global and local information about the target, enhancing model robustness and allowing the network to achieve satisfactory recognition performance even under sample-constrained conditions. Experimental results on a 10-class vehicle SAR image dataset demonstrate the effectiveness of the proposed approach.

Ship detection in polarimetric synthetic aperture radar (PolSAR) images has attracted widespread attention in recent years. However, pixel level detection methods are heavily affected by inherent speckle noise. In this letter, we proposed a detection method that enhances the ship-sea contrast beforehand by combining local statistical saliency and scattering mechanism coherence in superpixel-level. Firstly, simple linear iterative clustering (SLIC) based segmentation method is adopted for PolSAR images to generate superpixels. Then, local saliency is calculated based on superpixel-level similarity from the perspective of statistical characteristics. Based on this, the superpixel-level modified polarimetric coherence metric is obtained from the perspective of physical scattering mechanisms, which can help distinguish small ships with low saliency and strong sea clutters with high saliency. Ship detection is achieved by combining the two features above. The experimental results based on real PolSAR data show that compared with other classic and state-of-the-art methods, the proposed method has improved the figure of merit by at least 4.28% and has increased the target clutter ratio by at least 8.43 decibel (dB) on average.

Highly variable polarimetric signatures caused by complex structures in built-up areas make interpretation of these scattering behaviors intractable for PolSAR remote sensing. This paper proposes a fine polarimetric decomposition method and derives several products to finely simulate the scattering mechanisms of urban buildings, thus fulfilling its use for effective surveillance. First, through theoretically establishing the roll-invariant condition for a completely general scatterer, a roll-invariant cross polarization (RICP) scattering model is constructed, which characterizes the cross polarization scattering in the manner of planar structure distribution. Second, by designing a root-discriminant-based parameter inversion strategy, a fine seven-component decomposition is proposed, which achieves the complete physical interpretation of matrix elements and reasonable inversion of model parameters. Third, by analyzing the external and internal scattering difference, the derivative products, i.e., scattering contribution synthesizers are derived for built-up area monitoring. Experimental results derived from real PolSAR data confirm the superiority and effectiveness of the constructed descriptors on the one hand. On the other hand, the extensibility of fine polarimetric decomposition in specific remote sensing is also explicitly demonstrated.

In the last decades, how to detect ships with polarimetric synthetic aperture radar (PolSAR) has become one hot topic. Unfortunately, most of the existing ship detection methods cannot well detect small ships with weak backscattering. To deal with this issue, a ship detection matrix named complete polarimetric covariance matrix [CP] was recently proposed from the perspective of spatial information utilization. Although it is able to improve small ships’ target-to-clutter ratio (TCR) values, its calculation strategy still needs to be rethought due to the possible information loss of some ships. Besides, its mathematical characteristic (i.e., not positive semidefinite) also limits the successful applications of some existing polarimetric theories to it. To overcome these two drawbacks, we here develop an information reconstruction-based polarimetric covariance matrix [IC]. In brief, one new difference calculation strategy is first performed on the Sinclair matrix [$S$], so as to reconstruct its information, by which a feature vector$v$is subsequently extracted with the Lexicographic matrix basis. Then, via further performing an outer product operation on$v$, the matrix [IC] is proposed. Meanwhile, to demonstrate the effectiveness of [IC] in ship detection, two different [IC]-based intensity detectors, respectively, named SPANIC and PEDIC, are designed as well. Experiments carried out on three GF-3 PolSAR datasets show that: 1) the proposed matrix [IC] has a better performance than [CP] and the original polarimetric covariance matrix [$C$] in ship detection and 2) compared to the total power detector SPAN and geometrical perturbation-polarimetric notch filter (GP-PNF), both SPANIC and PEDIC can better detect ships, especially the small ships.

Superpixel segmentation is essential to the rapid information extraction and image interpretation. In this paper, we develop a superpixel segmentation method for polarimetric synthetic aperture radar (PolSAR) images, utilizing minimum spanning tree algorithm (MST) to achieve a hierarchy of superpixels. Thereinto, the revised Wishart distance and region intensity distance is applied to accurately measure the dissimilarity between two neighboring pixels. The proposed method can generate superpixels of different scales in real time, so it has significant application value. The performance of the proposed method is validated on experimental PolSAR dataset from the ESAR system.

In recent years, deep learning has made breakthroughs in the field of computer vision, the single-stage detection algorithm represented by You Only Look Once (YOLO) has achieved satisfying detection results in SAR ship target detection. For the multi-scale problem of SAR ship targets in complex scenes, we proposed an improved YOLOv5 detection method using Convolutional Block Attention Module (CBAM) and Bidirectional Feature Pyramid Network (BiFPN). The CBAM module and BiFPN are added in YOLOv5 so that it can fully learn the feature information of space and channel dimensions, and enhance information fusion transfer between multi-scale targets. Experiments on our dataset show that the proposed YOLOv5 algorithm achieves 92.8% Average Precision (AP), which gains a 1.9% improvement in AP compared to the standard YOLOv5 algorithm in SAR ship target detection. The problem of missed detection of multi-scale targets is well solved.

Current synthetic aperture radar (SAR) ship detectors achieve excellent performance with sufficient samples while encountering degraded results when the sensors and imaging conditions change. The mismatch of view, shape, and illumination inevitably result in the variations of feature distribution between source domain and target domain, which will lead to detection performance degradation. Therefore, devising a detector with well transferability to new domains remains a challenging issue. To this end, this letter proposes a novel domain adaptive YOLOv5 framework for cross-domain SAR ship detection, which is composed of the following keypoints: 1) a cross-domain co- attention feature correlation module, which models spatial and semantic interdependencies by capturing pixel correspondence between source and target domain in a bidirectional way; 2) a multilevel feature alignment module, which constrains the inter-domain difference of features from different scales by inserting three domain classifiers; and 3) teacher–student mutual learning, which makes full use of unlabeled target data and iteratively generates higher-quality pseudo-labels, thus further improving a teacher model with narrowed domain gap. Model performance is evaluated on three SAR ship datasets, and comprehensive results demonstrate the superiority of our method on multiple domain transfer scenarios, i.e., cross resolution, cross-sensor adaptation, and cross-resolution adaptation under the same sensor.

Due to the orientation variation and structural complexity, designing generalized and unified features to highlight manmade target scattering from polarimetric synthetic aperture radar (PolSAR) data is challenging. Inspired by the thought of mathematical programming (MP) and coupled with the model-based decomposition, this article proposes two polarimetric features: scattering contribution combiner (SCC) and scattering contribution angle (SCA) for unified scattering characterization of manmade targets. To this end, a rotated dihedral scattering model is first constructed concerning the analogous difference reciprocal and sigmoid function transformations, which adequately reflects the transition of co- and cross-pol responses caused by the orientation variation. Along with the dipole-like compound scattering models and through designing a discriminant-based model solution method, a fine eight-component decomposition using full polarimetric information is proposed. Through skillfully employing the MP strategies, the proposed decomposition achieves the physical optimization of scattering modeling and reasonable inversion of model parameters. Thus, it can accurately describe the local structure scattering and remarkably improve the overestimation of volume scattering. Subsequently, by analyzing the significance distribution on targets of different scattering mechanisms, the SCC is constructed via the linear/nonlinear combination of scattering contributions on the one hand. On the other hand, by further mining the information implied in the scattering contributions, the SCA is proposed with the strategy of trigonometric function transformation. Experimental results conducted on real PolSAR data not only demonstrate the effectiveness and superiority of the constructed features but also exhibit a clear advantage of fine polarimetric decomposition in scattering understanding, which encourages the use of them for further applications.

In the above article[1], the average TCR values inTable IIwere incorrectly presented. The corrected table is given here:

To more effectively detect small ships, in this article, a novel region-based polarimetric covariance difference matrix [RP] is put forward, which mainly consists of two stages. Briefly speaking, in the first stage, a new pixel representation way is proposed to depict the spatial characteristics of pixel, through which the difference information related to pixel’s local region is calculated as well. In the second stage, the global region difference information of pixel is computed. Finally, we construct [RP] via fusing these two different kinds of information together with a balance factor$c$. Meanwhile, considering that the backscattering energy of ships is useful for ship detection, a new intensity-driven polarimetric notch filter (ID-PNFRP) is also derived from [RP]. Three different datasets are adopted to evaluate the effectiveness of [RP] and ID-PNFRP. Experimental results show that: 1) compared with the polarimetric covariance matrix [$C$] and the polarimetric covariance difference matrix [$P$], [RP] is more suitable for ship detection and 2) compared with the original geometrical perturbation-polarimetric notch filter (GP-PNF) and the total power detector SPAN, the proposed method ID-PNFRPcan better detect small ships with greater figure of merit (FoM) and target-to-clutter ratio (TCR) values.

In this letter, we first investigate the relationship between polarimetric covariance matrix [C] and complete polarimetric covariance difference matrix [CP], and then construct a scattering difference parameter SDP. Subsequently, a hybrid polarimetric covariance matrix [HC] is developed based on SDP for curing the disadvantage of [CP], that is the scattering difference information of small ships cannot be well contained in [CP]. By fusing the feature “1-SDP” and the power detector SPANHCderived from [HC] together, a novel ship detection method SPANSDPis finally proposed to detect ships. Experiments performed on the airborne SAR (AIRSAR) L-Band and GF-3 C-Band data verify that 1) SPANSDPcan detect small ships more accurately than other state-of-the-art methods and 2) [HC] is more effective in improving ship detectors' detection performances in comparison with [CP].

Object detection for optical remote sensing images have undergone rapid development in recent years, due to the advanced techniques of deep learning. However, the diverse objects with different scales and aspect ratios increase the difficulty of detection. In this paper, we improve the detection performance by applying guided anchor generation and feature fusion. In specific, the guided anchoring block directly predicts the positions and shapes of the anchor boxes, taking the place of sliding windows and preset shapes in the original region proposal network (RPN). Then, it can obtain diverse and adaptable anchor boxes, avoiding the generation of redundant and monotonous anchor boxes. In addition, the RoI features obtained from different pyramid levels are fused together to predict the categories and locations of the objects. The experiments conducted on DOTA dataset demonstrate the effectiveness of the proposed method.

This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use.

In this paper, a superpixel-based segmentation method is proposed for PolSAR images by utilizing hierarchical region merging and segment refinement. The loss of the energy function, which determines the consistency of two adjacent regions from the statistical aspect, is applied to guide the merging procedure. In addition to the edge penalty term, the homogeneity measurement is also employed to prevent merging the regions that are from different land covers or objects. Based on the merged segments, the segment refinement is applied to further improve the segmentation accuracy by iteratively relabeling the edge pixels. It uses a maximum a posterior (MAP) criterion using the statistical distribution of the pixels and the Markov random field (MRF) model. The performance of the proposed method is validated on an experimental PolSAR dataset from the ESAR system.

Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

Automatic target recognition has been widely studied over the years, yet it is still an open problem. The main obstacle consists in extended operating conditions, e.g.., depression angle change, configuration variation, articulation, and occlusion. To deal with them, this paper proposes a new classification strategy. We develop a new representation model via the steerable wavelet frames. The proposed representation model is entirely viewed as an element on Grassmann manifolds. To achieve target classification, we embed Grassmann manifolds into an implicit reproducing Kernel Hilbert space (RKHS), where the kernel sparse learning can be applied. Specifically, the mappings of training sample in RKHS are concatenated to form an overcomplete dictionary. It is then used to encode the counterpart of query as a linear combination of its atoms. By designed Grassmann kernel function, it is capable to obtain the sparse representation, from which the inference can be reached. The novelty of this paper comes from: 1) the development of representation model by the set of directional components of Riesz transform; 2) the quantitative measure of similarity for proposed representation model by Grassmann metric; and 3) the generation of global kernel function by Grassmann kernel. Extensive comparative studies are performed to demonstrate the advantage of proposed strategy.

Shape is considered to be one of the most promising tools to represent and recognise an object. In this study, an effective and rigorous shape matching algorithm is developed based on a new descriptor and relaxation labelling technique. For each contour point, the descriptor captures the distribution of all points within the shape region along the vector perpendicular to that from the centroid to the point. In addition to stable affine invariance, the descriptor is robust to noise since it makes use of all points in the shape region. The descriptor distance is used to initialise the contour point matching probability, and relaxation labelling technique is utilised to update the matching probability using a new compatibility coefficient function, which is defined based on the shape projection preserving characteristic. The experiments on synthetic and real remote sensing data are provided to test the performance of the authors’ proposed algorithm. Compared to other four state‐of‐the‐art contour‐based shape matching algorithms, their algorithm is more robust and capable of shape matching under affine transformations and noise.

The use of contextual information is beneficial to improve both the accuracy and reliability of image classification. Based on the robust fuzzy${c}$-means (RFCM) clustering method and an adaptive Markov random field model, this letter proposes a contextual${H}/{\bar {\alpha }}$classifier for polarimetric synthetic aperture radar images. At each iterative step of RFCM clustering, the prior probability extracted from the local neighborhood is combined with the fuzzy membership derived from inherent polarimetric characteristics, thus the enhanced fuzzy membership is more reliable. In addition, an adaptive smoothing factor is proposed for use during contextual information retrieval, which can prevent oversmoothing and preserve the local spatial details. The experimental results implemented using AIRSAR and ESAR L-band data validate the efficacy of the proposed method. Compared with the iterated Wishart classifier and fuzzy${H}/{\bar {\alpha }}$classifier, the proposed method significantly improves the classification accuracy, with less noise and increased preservation of details.

The classic constant false alarm rate edge detector with a rectangle-shaped filter has been proven to be effective and widely used in polarimetric synthetic aperture radar (PolSAR) images. However, in practical use, the assumption of complex Wishart distribution is often not respected, particularly in heterogeneous urban areas. In addition, as a simple smoothing filter, the rectangle-shaped window is often shown to be easy to incur false edge pixels near true edges. Therefore, its performance is limited. To overcome this restriction, we propose a new edge detector for PolSAR images, which utilizes the spherically invariant random vector product model to estimate the normalized covariance matrix for each pixel, and then replace the rectangle-shaped filter with a Gauss-shaped filter. The performance of our proposed methodology is presented and analyzed on two real PolSAR data sets, and the results show that the new edge detector attains better performance than the classic one, particularly for urban areas.

Moment has been widely used for contour matching. To use the moment to achieve contour matching under affine transformations, the affine‐invariant support point set (SPS) should be constructed first. Then, a novel method of acquiring SPS based on the contour projection (SPS‐CP) is proposed here. For an arbitrary selected contour point, the contour is projected onto the line vertical to the vector connecting the contour centroid and the selected point, and the contour points with the sampled projection values are picked up to form the SPS‐CP of the point. SPS‐CP which captures the global structure of the contour is stably affine‐invariant. Experiments on synthetic and real data demonstrate that moments generated from SPS‐CP outperform those generated from SPSs sampled by uniform spacing or affine length.