SAR Target Depression Angle Invariant Recognition of Few-Shot Learning Via Dense Graph Prototype 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.

DARPA/Air Force Research Laboratory Moving and Stationary Target Acquisition and Recognition (MSTAR) program is developing state-of-the-art model based vision approach to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The model-based approach requires using off-line developed target models in an on-line hypothesize-and-test manner to compare predicted target signatures with image data and output target reports. Central to this model-based ATR is the PEMS (Predict-Extract-Match-Search) subsystem. The Search module is critical to PEMS by providing intelligent control to traverse the hypothesis feature space. A major MSTAR goal is to demonstrate robust ATR for variations in targets including partially hidden targets. This paper will provide an update on the technology being developed under MSTAR and the status of this model based ATR research, specifically concentrating on the Search Module.

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.

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

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.

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

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 existing synthetic aperture radar (SAR) automatic target recognition (ATR) methods have shown impressive results in static scenarios, yet the performance drops sharply as new categories of targets are continuously increased. In response to this problem, this letter proposes a novel ATR method named multi-level adaptive knowledge distillation network (MLAKDN) to achieve incremental SAR target recognition. To be specific, an adaptive weighted distillation strategy is first proposed, which can alleviate the model from forgetting the knowledge of old categories by distilling multi-stage soft label information of old categories at the classification level. Then, a feature distillation method based on gradient maximum criterion is developed to filter and distill discriminative features, so as to further recall more knowledge of old categories at the feature level. Meanwhile, a model rebalancing technique is designed to effectively strike the balance of the model on new categories and old categories. Finally, a weighted incremental classification loss is presented to train the whole model. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset and the synthetic and measured paired labeled experiment (SAMPLE) dataset illustrate that the proposed method is superior to some state-of-the-arts for incremental SAR target recognition tasks.

Data augmentation is one of the most straightforward methods to alleviate the problem of data scarcity in deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR). For example, novel view generation based on physical electromagnetic models or deep generative models. Besides the data scale, the quality of generated SAR images also has a significant impact on the model's performance. Therefore, it is crucial to objectively and effectively assess the quality of generated SAR images. In this work, a relative recognition accuracy score (RRS)-based evaluation criterion is proposed. The core idea of the proposed evaluation criterion is to control variables. First, the measured SAR images are gradually replaced by the generated SAR images at a progressive hybrid rate (HR) to obtain hybrid training datasets with varying HR levels. Then, the recognition model (e.g., AconvNet) is trained on both the all-measured dataset and hybrid datasets with varying HR to plot the reference recognition accuracy (RRA) and the hybrid recognition curve (HRC). Finally, the integral difference between RRA and HRC is defined as the RRS of the generated SAR dataset. Specifically, we employ proportional random sampling and multiple sampling averaging to alleviate selection bias. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset validate the rationality and effectiveness of the proposed RRS-based evaluation criterion.

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.

The deep learning has some new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). By introducing different constraints, deep belief network (DBN) has become apply to SAR target recognition recent years, but the existing DBN algorithms have some questions including the high training epochs, low recognition rate and complex structure. Therefore, an algorithm based on guided reconstruction and weighted norm-constrained DBN is proposed. Firstly, in order to reduce the dimension of the image output feature, increase the speed of preprocessing, generate a one-dimensional image vector and normalized, a SAR target classification algorithm with two-scale fusion character based on guided filter reconstruction algorithm is introduced. Then, the sparse feature extraction of SAR image is carried out by weighted norm-constrained DBN. By the regularization constraint of the probability distribution, the algorithm can minimize the joint probability distribution of visual layer and hidden layer by samples. The low-dimensional feature is further improved based on the generalized optimization of norm-constrained RBM. Finally, by a regularized Softmax which can classify the targets and obtain output results. The experimental results show that the SAR target recognition algorithm based on the guided reconstruction and weighted constrained deep confidence network not only improves the target recognition performance and generalization ability, but also reduces the output feature dimension and network training times, and the recognition performance of the algorithm is further improved.

Deep learning (DL) techniques recently have attracted much attention in the synthetic aperture radar (SAR) automatic target recognition (ATR). Due to the coherent imaging pattern, SAR images inherently suffer from the speckle noise. To mitigate its influence, this letter proposes a joint convolutional neural network (J-CNN) for simultaneous despeckling and classification of SAR targets. It integrates a two-step process in the CNN framework but without the pooling operation during the despeckling phase. Then, a new loss function is introduced, and its partial derivatives with respect to weights are given for the training of J-CNN. Finally, comparative experiments with some classical network models are carried out based on synthetic SAR target images. The results demonstrate that the proposed method not only significantly outperforms other models under strong speckle noise condition but also has an efficient architecture with fewer weight parameters.

The focus of this paper is predicting the bounds on performance of a vote-based object recognition system, when the test data features are distorted by uncertainty in both feature locations and magnitudes, by occlusion and by clutter. An improved method is presented to calculate lower and upper bound predictions of the probability that objects with various levels of distorted features will be recognized correctly. The prediction method takes model similarity into account, so that when models of objects are more similar to each other, then the probability of correct recognition is lower. The effectiveness of the prediction method is validated in a synthetic aperture radar (SAR) automatic target recognition (ATR) application using MSTAR public SAR data, which are obtained under different depression angles, object configurations and object articulations. Experiments show the performance improvement that can obtained by considering the feature magnitudes, compared to a previous performance prediction method that only considered the locations of features. In addition, the predicted performance is compared with actual performance of a vote-based SAR recognition system using the same SAR scatterer location and magnitude features.

The traditional Synthetic Aperture Radar Automatic Target Recognition (SAR/ATR) algorithm can train a sufficient number of known class samples and classify the samples in the test set. However, if the old model is trained only with the new class samples, the old class samples’ knowledge is easily forgotten by the new model, which is called catastrophic forgetting. The reason is that the model only fits the distribution of current training samples, so training the whole data set is necessary. Due to the limitation of storage resources, it is often not feasible to retain the whole data set. In order to avoid this phenomenon, a small number of old class samples can be kept to train with the new class samples. Therefore, how to select the old class samples becomes the key point. In this paper, the Density Coverage-Based Exemplar Selection (DCBES) is proposed to choose the key samples of the old class. DCBES selects samples based on the metric learning theory and the set covering theory. First, the metric learning theory is used to measure the similarity between samples and to obtain the density range of samples. Then the exemplar selection problem is considered a set covering problem, to select a fixed number of exemplars to achieve the maximum coverage of the class density range. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our method is superior to other exemplar selection methods and achieves the best results.

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>.