Performance modeling of vote-based object recognition

Synthetic aperture radar (SAR) automatic target recognition is an important application in SAR. How to extract features has restricted the application of SAR technology seriously. In this paper, a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed, which integrates class and neighborhood information. This method can reinforce discriminative power using maximum interclass distance, so it can improve recognition rate effectively.

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.

Recently, significant advancements have been made in few-shot learning (FSL) methods based on metric learning, which have been widely applied to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). These methods typically require experimental samples to exhibit sufficiently small intra-class distances. However, in practical applications, SAR target samples are often acquired under varying depression angles, resulting in mixed images. The inherent feature deviations caused by variations in depression angles lead to increased intra-class distances in the feature distribution, which adversely affects recognition performance. To address this challenge, we propose a novel approach called the Dense Graph Prototype Network (DGP-Net). DGP-Net addresses the feature deviation problem by learning potential features and utilizes feature distribution modeling for classification. Specifically, by leveraging the information propagation mechanism of a densely connected Graph Convolutional Network (GCN), potential features are iteratively learned while retaining previous features, thereby clustering samples of the same class with different elevation angles and eliminating feature deviations. To avoid sampling examples located at the edges of the distribution in FSL tasks, which may lead to inaccurate GCN information propagation, we introduce a ”prototype node” mechanism to enhance the robustness of the model. We conduct experiments using samples from the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset with varying elevation angles for the same class. Experimental results on this dataset demonstrate the superior performance of DGP-Net. Specifically, in 3-way and 5-way classification tasks, the recognition accuracy of DGP-Net improved by over 4% compared to state-of-the-art (SOTA) methods. The source code for DGP-Net is available at https://github.com/Zhou-xy99/DGP-Net.

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.

Attributed scattering center is one of important features of Synthetic Aperture Radar (SAR) image. In this paper, a method for the matching of attributed scattering centers and its application to SAR target recognition is proposed. First, the attributed scattering centers of the test SAR image and template SAR images are extracted on the basis of the attributed scattering model. Second, the Hungarian algorithm is employed to match the two scattering center sets. Based on the one to one correspondence, we design a new similarity measure to evaluate the similarity between the two scattering center sets that will decide the target type of the test image. The similarity measure considers the effects of each individual scattering center, single matching pair, and missing alarms and false alarms; thus, it is more comprehensive. The experiment based on moving and stationary target acquisition and recognition database demonstrates the validity of the proposed method.

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.

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.

A Study of Dempster-Shafer’s Theory of Evidence in Comparison to Classical Probability Combination Scott J. Seims This thesis is an assessment on the effectiveness of Dempster-Shafer’s Theory of Evidence in comparison to Classical Probabilistic Combination as it applies to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). A three feature based classifier (peaks, corners and edges) ATR system is presented. These classifiers are assumed to be independent. The results of both the weight-based Maximum Likelihood and Dempster-Shafer’s Theory of Evidence data fusion techniques are presented. Using Dempster-Shafer an accuracy of 77.50 percent is obtained, which is less than the 86.25 percent accuracy of target-specific weight-based Maximum Likelihood[6]. Inagaki’s Unified Combination Rule is implemented as a means to increase SAR ATR accuracy and explore further modifications of Dempster-Shafer. Inagaki’s Unified Combination Rule contains Yager’s and Dempster’s Combination Rules as well as Inagaki’s Extra Rule[17][10]. The maximum accuracy achieved using Inagaki’s Unified Combination Rule was 75.00 percent. It was concluded that this application lends itself better to classical probabilistic combination. Due to the single sensor (SAR) and the quasi-independence of the three feature based classifiers, Dempster-Shafer’s Theory of Evidence can not be utilized to its full potential. This thesis is a continuation of Hausdorff Probabilistic Feature Analysis in SAR Image Recognition by Chessa Guilas[6]. Thesis research is directed by Dr. John A. Saghri and sponsored by Raytheon Space & Airborne Systems, El Segundo, California.

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.

Synthetic aperture radar (SAR) automatic target recognition (ATR) is extensively applied in both military and civilian sectors. Nevertheless, test and training data distribution may differ in the open world. Therefore, SAR out-of-distribution (OOD) detection is important because it enhances the reliability and adaptability of SAR systems. However, most OOD detection models are based on maximum likelihood estimation (MLE) and overlook the impact of data uncertainty, leading to overconfidence output for both in-distribution (ID) and OOD data. To address this issue, we consider the effect of data uncertainty on prediction probabilities, treating these probabilities as random variables and modeling them using Dirichlet distribution. Building on this, we propose an evidential uncertainty aware mean squared error (UMSE) loss function to guide the model in learning highly distinguishable output between ID and OOD data. Furthermore, to comprehensively evaluate OOD detection performance, we have compiled and organized some publicly available data and constructed a new SAR OOD detection dataset named SAR-OOD. Experimental results on SAR-OOD demonstrate that the UMSE approach achieves state-of-the-art (SOTA) performance. The code and data are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Xiaoyan-Zhou/UMSE-SAR-OOD-Detection</uri>.

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

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.

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.

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