Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

Convolutional neural network(CNN) has achieved great success in optical image recogniton, and has been applied in research of synthetic aperture radar(SAR) automatic target recognition(ATR) recently. However, in real SAR systems, SAR images are usually compressed for transmission due to the limited wireless bandwidth, and few researches have evaluated the influence of image compression on SAR ATR task. In this paper, an efficient CNN architecture based on inception module and batch normalization layer is proposed for SAR ATR tasks, and the impact of image compression on SAR ATR is evaluated based on the proposed model. The experiments are based on MSTAR dataset, and the test images are compressed by Set Partitioning in Hierarchical Trees(SPIHT) algorithm with different compression ratio. Experimental results show that the proposed CNN model achieves a state-of-the-art classification accuracy of 99.29% on original MSTAR dataset, and can still get high classification accuracy above 90% even SAR images in test set are compressed by nearly one hundred times, which reveals that moderate compression of SAR images has little influence on SAR ATR tasks, and this validates the practicability of applying the proposed model to real SAR ATR systems.

Deep neural network-based synthetic aperture radar (SAR) automatic target recognition (ATR) systems are susceptible to attack by adversarial examples, which leads to misclassification by the SAR ATR system, resulting in theoretical model robustness problems and security problems in practice. Inspired by optical images, current SAR ATR adversarial example generation is performed in the image domain. However, the imaging principle of SAR images is based on the imaging of the echo signals interacting between the SAR and objects. Generating adversarial examples only in the image domain cannot change the physical world to achieve adversarial attacks. To solve these problems, this article proposes a framework for generating SAR adversarial examples in a 3D physical scene. First, adversarial attacks are implemented in the 2D image space, and the perturbation in the image space is converted into simulated rays that constitute SAR images through backpropagation optimization methods. The mapping between the simulated rays constituting SAR images and the 3D model is established through coordinate transformation, and point correspondence to triangular faces and intensity values to texture parameters are established. Thus, the simulated rays constituting SAR images are mapped to the 3D model, and the perturbation in the 2D image space is converted back to the 3D physical space to obtain the position and intensity of the perturbation in the 3D physical space, thereby achieving physical adversarial attacks. The experimental results show that our attack method can effectively perform SAR adversarial attacks in the physical world. In the digital world, we achieved an average fooling rate of up to 99.02% for three objects in six classification networks. In the physical world, we achieved an average fooling rate of up to 97.87% for these objects, with a certain degree of transferability across the six different network architectures. To the best of our knowledge, this is the first work to implement physical attacks in a full physical simulation condition. Our research establishes a theoretical foundation for the future concealment of SAR targets in practical settings and offers valuable insights for enhancing the attack and defense capabilities of subsequent DNNs in SAR ATR systems.

Automatic target recognition (ATR) of synthetic aperture radar (SAR) images is performed on either global or local features. The global features can be extracted and classified with high efficiency. However, they lack the reasoning capability thus can hardly work well under the extended operation conditions (EOCs). The local features are often more difficult to extract and classify but they provide reasoning capability for EOC target recognition. To combine the efficiency and robustness in an ATR system, a hierarchical fusion of the global and local features is proposed for SAR ATR in this paper. As the global features, the random projection features can be efficiently extracted and effectively classified by sparse representation-based classification (SRC). The physically relevant local descriptors, i.e., attributed scattering centers (ASCs), are employed for local reasoning to handle various EOCs like noise corruption, resolution variance, and partial occlusion. A one-to-one correspondence between the test and template ASC sets is built by the Hungarian algorithm. Then, the local reasoning is performed by evaluating individual matched pairs as well as the false alarms and missing alarms. For the test image to be recognized, it is first classified by the global classifier, i.e., SRC. Once a reliable decision is made, the whole recognition process terminates. When the decision is not reliable enough, it is passed to the local classifier, where a further classification by ASC matching is carried out. Therefore, by the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system. Extensive experiments on the moving and stationary target acquisition and recognition data set demonstrate that the proposed method achieves superior effectiveness and robustness under both SOC and typical EOCs, i.e., noise corruption, resolution variance, and partial occlusion, compared with some other SAR ATR methods.

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety concerns when applying DNNs to high-stakes SAR ATR applications. Therefore, enhancing the adversarial robustness of DNNs is essential for applying DNNs to modern real-world SAR ATR systems. Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers). The proposed SMGAA consists of two parts: 1) a parametric scattering model and corresponding imaging method and 2) a customized gradient-based optimization algorithm. First, we introduce the effective Attributed Scattering Center Model (ASCM) and a general imaging method to describe the scattering behavior of typical geometric structures in the SAR imaging process. By further devising several strategies to take the domain knowledge of SAR target images into account and relax the greedy search procedure, the proposed method does not need to be prudentially finetuned, and can efficiently find the effective ASCM parameters to fool the SAR classifiers and facilitate the robust model training. Comprehensive evaluations on the MSTAR dataset show that the adversarial scatterers generated by SMGAA are more robust to perturbations and transformations in the SAR processing chain than the currently studied attacks, and are effective to construct a defensive model against the malicious scatterers.

The traditional multi-view synthetic aperture radar (SAR) automatic target recognition (ATR) is based on the assumption of viewpoint integrity of the dataset, requiring that the viewpoint information of all samples participating in the recognition is complete. To address the problem that SAR ATR is difficult under the training condition of an incomplete view sample set, a new cross-view SAR target recognition method based on view correlation extension and multi-task learning is proposed. To mine the complementary information between multi-view SAR images, the training datasets are clustered into subsets of different views based on the correlation coefficient. To improve the accuracy of cross-view recognition, we adopt the strategy of multi-view subsets corresponding to multi-task kernel sparse representation. For the decision values of each view subset, the fusion error is obtained by linear weighting. Finally, the total reconstruction errors accumulated from all tasks are identified. Experimental results on the MSTAR database in cross-view conditions show that our method outperforms the widely-used SAR ATR methods in several performance indicators.

Few-shot SAR automatic target recognition based on Conv-BiLSTM prototypical network

The existing deep learning studies on synthetic aperture radar (SAR) automatic target recognition (ATR) mainly focus on the utilization of the amplitude of SAR image via convolutional neural network (CNN), while the electromagnetic scattering information is rarely considered. Given that scattering centers (SCs) can characterize the target’s electromagnetic scattering characteristics and describe the target’s physical structure information, the SC feature should be helpful for SAR ATR. Therefore, we propose a novel SAR ATR method that combines electromagnetic scattering information and graph convolutional network (GCN) effectively and directly. Specifically, we model each extracted SC as a node to convert the SCs into graph data. The constructed graph is learned via GCN to describe the target’s physical structure information, where the features of different GCN layers are fused to avoid the over-smoothing of GCN. Label smoothing is combined with GCN for the first time to alleviate the overfitting caused by the limited training data. To the best of our knowledge, this study is the first to introduce the GCN for effectively utilizing the SCs, proving that the structural characteristics of the SCs of SAR targets are remarkably beneficial for recognition. Extensive experimental results on the measured moving and stationary target acquisition and recognition (MSTAR) dataset show that our method can obtain superior recognition performance compared with the existing methods.

Combination of global and local filters for robust SAR target recognition under various extended operating conditions

This paper presents an enhanced ensemble classification framework for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) under diverse operational conditions, including Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed method integrates the strengths of Residual Neural Networks (ResNet) replacing Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and template matching, leveraging majority voting to combine their complementary capabilities. The ensemble framework achieves improved robustness and classification accuracy across varied scenarios. The methodology employs ResNet, a deep learning architecture known for its superior feature extraction and classification capabilities, replacing AlexNet to address limitations in generalization and consistency. ResNet demonstrated better performance with average accuracies of 92.67% under SOC and 88.9% under EOC, showing consistent results across all six target classes, as compared to the CNN-based ensemble approach with average accuracies of 90.30% under SOC and 87.22% under EOC. The SVM is employed for its robustness in handling overfitting and classifying features extracted from 16 region properties. Template matching is included for its resilience in challenging conditions where deep learning techniques may underperform. Experimental validation using the MSTAR dataset, a standard benchmark for SAR ATR, highlights the effectiveness of this ensemble approach. The results confirm significant improvements in classification accuracy and robustness over individual classifiers, demonstrating the practical applicability of the ensemble approach to real-world SAR ATR challenges. This research advances SAR ATR by addressing critical challenges, including noise, occlusion, and variations in viewing angles while achieving high classification performance under diverse conditions. The integration of ResNet further enhances the framework’s adaptability and reliability.

With the development of deep learning (DL) and synthetic aperture radar (SAR) imaging techniques, SAR automatic target recognition has come to a breakthrough. Numerous algorithms have been proposed and competitive results have been achieved in detecting different targets. However, due to the influence of various sizes and complex background of ships, detecting multiscale ships in SAR images is still challenging. To solve the problems, a novel network, called attention receptive pyramid network (ARPN), is proposed in this article. ARPN is a two-stage detector and designed to improve the performance of detecting multiscale ships in SAR images by enhancing the relationships among nonlocal features and refining information at different feature maps. Specifically, receptive fields block (RFB) and convolutional block attention module (CBAM) are employed and combined reasonably in attention receptive block to build a top-down fine-grained feature pyramid. RFB, composed of several branches of convolutional layers with specifically asymmetric kernel sizes and various dilation rates, is used for grabbing features of ships with large aspect ratios and enhancing local features with their global dependences. CBAM, which consists of channel and spatial attention mechanisms, is utilized to boost significant information and suppress interference caused by surroundings. To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset and two large-scene SAR images. The detection results illustrate that competitive performance has been achieved by our method in comparison with several CNN-based algorithms, e.g., Faster-RCNN, RetinaNet, feature pyramid network, YOLOv3, Dense Attention Pyramid Network, Depth-wise Separable Convolutional Neural Network, High-Resolution Ship Detection Network, and Squeeze and Excitation Rank Faster-RCNN.

In recent years, convolutional neural network (CNN) has been increasingly considered as a promising technology for military and homeland security applications. The fusion of CNN and Support vector machine (SVM), a popular traditional machine learning approach, has received intensive attention in the field of synthetic aperture radar (SAR) automatic target recognition (ATR). This paper, firstly, discusses the effects of some preprocessing and image enhancement methods on the performance of SAR ATR, starting with the pre-trained AlexNet in a transfer-learning based approach. Secondly, the architecture of AlexNet is modified to form a new model suitable for SAR ATR. Finally, we propose a hybrid model associated with the success of the learning feature of our CNN model and the ability of SVM to process high-dimensional dataset effectively. To evaluate the proposed method, experiments are performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database. The comparative results demonstrate that these preprocessing and enhancement methods prior to the deep-learning process are not necessary since the feature representation ability of AlexNet is already powerful. Furthermore, experimental results on the benchmark MSTAR dataset illustrate the effectiveness of the proposed new model. On classification of ten-class targets, the commonly used translation augmentation for training data has been performed. By combining the CNN and SVM, the classification accuracy percentages can be slightly improved for our proposed new model.

Decades of research have been dedicated to overcoming the obstacles inherent in synthetic aperture radar (SAR) automatic target recognition (ATR). The rise of deep learning technologies has brought a wave of new possibilities, demonstrating significant progress in the field. However, challenges like the susceptibility of SAR images to noise, the requirement for large-scale training datasets, and the often protracted duration of model training still persist. This paper introduces a novel data augmentation strategy to address these issues. Our method involves the intentional addition and subsequent removal of speckle noise to artificially enlarge the scope of training data through noise perturbation. Furthermore, we propose a modified network architecture named weighted ResNet, which incorporates residual strain controls for enhanced performance. This network is designed to be computationally efficient and to minimize the amount of training data required. Through rigorous experimental analysis, our research confirms that the proposed data augmentation method, when used in conjunction with the weighted ResNet model, significantly reduces the time needed for training. It also improves the SAR ATR capabilities. Compared to existing models and methods tested, the combination of our data augmentation scheme and the weighted ResNet framework achieves higher computational efficiency and better recognition accuracy in SAR ATR applications. This suggests that our approach could be a valuable advancement in the field of SAR image analysis.

In the field of synthetic aperture radar (SAR) automatic target recognition (ATR), deep learning-driven methods perform well when numerous training samples are available. However, in practical SAR application scenarios, the number of available training samples is often scarce due to the limitation of observation conditions, which leads to major challenges for deep learning-driven methods in real-world deployment. Currently, the problem of ATR with only a few training samples has attracted much attention in the SAR field. In this article, we put forward an ATR method called azimuth-guided feature embedding network with dual inference mechanism (AGFEN-DIM) to achieve fewshot SAR target recognition. To be specific, the feature extraction model, i.e. azimuth-guided feature embedding network (AGFEN) is composed of the azimuth embedding module (AEM) and the dynamic feature embedding module (DFEN). Among them, AEM resorts to a set of azimuth-parameterized convolution kernels, aiming to make full use of information relevant to the current radar imaging environment. The proposed DFEN is capable of dynamically extracting the discriminative features of the target according to the input image under the guidance of azimuth-parameterized convolutional kernels. The dual inference mechanism (DIM) effectively integrates the inference results of improved prototype classification and label propagation strategy to boost the reliability and robustness of the recognition model for few-shot SAR target recognition tasks. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed AGFEN-DIM surpasses many state-of-the-art SAR ATR methods in various application scenarios.

Deep neural networks have recently demonstrated state-of-the-art accuracy on public Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) benchmark datasets. While attaining competitive accuracy on benchmark datasets is a necessary feature, it is important to characterize other facets of new SAR ATR algorithms. We extend this recent work by demonstrating not only improved state-of-the-art accuracy, but that contemporary deep neural networks can achieve several algorithmic traits beyond competitive accuracy which are necessitated by operational deployment scenarios. First, we employ several saliency map algorithms to provide explainability and insight into understanding black-box classifier decisions. Second, we collect and implement numerous data augmentation routines and training improvements both from the computer vision literature and specific to SAR ATR data in order to further improve model domain adaptation performance from synthetic to measured data, achieving a 99.26% accuracy on SAMPLE validation with a simple network architecture. Finally, we survey model reproducibility and performance variability under domain adaptation from synthetic to measured data, demonstrating potential consequences of training on only synthetic data.

Due to the extensive information in multi-view images, multi-view synthetic aperture radar (SAR) automatic target recognition (ATR) has attracted much attention. However, most current algorithms ignore the integration of multi-view topological features intrinsically related to the characteristics of SAR images. Moreover, as well as not thoroughly exploring the inherent coupling relationship, the multi-view feature fusion modules in these algorithms are also prone to cause the serve parameter burden and insufficient generality of the entire ATR model. To tackle these issues, an ATR model called multi-view visual and topological feature coordination aggregation (MVT-CA) is proposed. First, two parallel feature extraction modules are employed to extract multi-view visual and topological features independently. Specifically, the topological feature extraction module (TFM) based on a hypergraph neural network is designed to extract the implicit topological features by aggregating the context refinement features of key points within the SAR images. Subsequently, a parameterfriendly feature coordination aggregation module (FCAM) with visual and topological consistency is introduced, which effectively integrates multi-view features to generate a unified representation for classification while enhancing the generality of the entire ATR model. Experimental results on the Moving and Stationary Target Recognition (MSTAR) and the Full Aspect Stationary TargetsVehicle (FAST-Vehicle) datasets verify the effectiveness of our MVTCA model, even in scenarios involving severe background noise and target deformation