Synthetic Aperture Radar Automatic Target Recognition Methods Research Articles

Enhanced Prototypical Network with Customized Region-Aware Convolution for Few-Shot SAR ATR

With the prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning-based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning-based methods can achieve outstanding recognition performance on the condition that an abundance of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Therefore, SAR target recognition in the situation of few-shot, where only a scarce few labeled samples are available, is a fundamental problem that needs to be solved. In this paper, a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) is put forward to specially tackle the few-shot SAR ATR tasks. To be specific, a feature-extraction network based on a customized and region-aware convolution is first developed. This network can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, an enhanced prototypical network is proposed. This network can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset, the OpenSARShip dataset, and the SAMPLE+ dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

A Feasibility Analysis of the Use of ISAR Training Data in Machine Learning-Based SAR ATR

Processing of synthetic aperture radar (SAR) images for automatic target recognition (ATR) is a critical application especially in military surveillance. In particular, numerous machine learning-based SAR ATR methods have been proposed for this task. However, data training and testing stages of all these methods are based on the exploitation of SAR signatures of the target under investigation. Considering the high variability of radar targets, obtaining such signature data is obviously a costly and time consuming process. In this study, therefore, a feasibility analysis of the use of inverse-SAR (ISAR) training data in SAR ATR has been made for the first time. The turntable ISAR and circular SAR images of three different vehicles are used in training and testing is performed by means of SAR images of three similar targets within the publicly available MSTAR dataset. Also, three most prominent machine learning methods, namely KNN, SVM and ANN are used in conjunction with three different feature extraction algorithms namely, GLRLM, GLSZM and GLCM. The obtained results reveal that the GLCM+SVM algorithm pair is the most effective model with 85% accuracy.

A Comprehensive Survey on SAR ATR in Deep-Learning Era

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field.

Synthetic Aperture Radar Target Recognition Based on Multimodule Image Enhancement Network

Synthetic aperture radar (SAR) has been widely used in recent years, and SAR automatic target recognition (ATR) has become a research hotspot. Most of the existing SAR ATR methods focus on the network structure design and increasing data volume but omit image quality and real-time processing. We design a multimodule image enhancement network (MMIE-Net) to solve these problems, which include the target extraction module, the image processing module, and convolutional neural networks (CNNs). First, we use the target extraction module and the image processing module to enhance the quality of raw SAR images. Then we design a suitable network for SAR image recognition, which is simple, lightweight, and recognizable. The experiment was mainly carried out on the MSTAR dataset, which can be divided into two categories, Standard Operating Condition (SOC) and Extended Operating Condition (EOC). The identification accuracy, the parameter storage space, and the depth of the model are considered as the criterion. The experimental results show that, compared with other methods, the proposed method not only ensures the simple structure of the network model but also has better recognition accuracy. Additionally, our method is robust and stable to large depression angle variation as well.

초해상화 기반 CNN을 이용한 군사용 SAR 자동표적인식 모델 연구

Herein, we propose employing the super-resolution-based convolutional neural network (CNN) to design the automatic target recognition (ATR) of military synthetic aperture radar (SAR) images. Previous SAR ATR methods showed a good recognition performance with a low depression angle, but poor performance with a high depression angle. In a warfighting environment, good recognition performance is required even with a high depression angle. To address this issue, we combine the super-resolution method and the CNN. In comparison with the conventional VGGnet with a high depression angle, the proposed super-resolution-based CNN showed a 3%-4% improvement in accuracy. The MSTAR SAR dataset was utilized for validation.

Adversarial Deception Against SAR Target Recognition Network

Synthetic Aperture Radar (SAR) automatic target recognition (ATR) technology is one of the key technologies to achieve intelligent interpretation for SAR images. With the rapid development of deep learning, deep neural networks have been successively used in SAR ATR and show priority in comparison with the conventional methods. Recently, more and more attention is paid to the robustness of deep learning based SAR ATR methods. The reason is that maliciously modified and imperceptible adversarial images can deceive the SAR ATR methods which are based on the deep neural networks. In this paper, we propose a novel SAR ATR adversarial deception algorithm, which fully considers the characteristics of SAR data. Our method can obtain the satisfactory perturbations with a higher deception success rate, a higher recognition confidence, and a smaller perturbation coverage than other state-of-the- art methods for the SAR images. Experimental results using the MSTAR dataset and OpenSARShip dataset demonstrate the effectiveness of our method. The proposed adversarial deception method can be used in the applications such as SAR dataset protection, SAR sensor design and SAR image quality evaluation.

Recognition in Label and Discrimination in Feature: A Hierarchically Designed Lightweight Method for Limited Data in SAR ATR

Synthetic aperture radar (SAR) automatic target recognition (ATR) is an essential field in SAR application. However, a sufficient number of labeled training SAR images for each target type plays a crucial role in existing SAR ATR methods, while the acquisition and annotation of SAR images are difficult and time-consuming in practice. Therefore, the recognition under the limited labeled training SAR images is the basic and crucial problem in SAR application. In this paper, we propose a novel hierarchically-designed lightweight method (HDLM) by recognition in label and discrimination in feature to address the problem of limited data in SAR ATR. The proposed method is hierarchically designed from top to bottom. In the top phase, the framework is constructed by dual loss to force the deep model to optimize by label recognition and feature discrimination, which is noted as recognition in label and discrimination in feature. In the middle phase, the architecture of the network is built up using a novel lightweight extractor and multi-level cross fusion to boost the amount and diversity of the features for the framework. In the bottom phase, two modules, coordinate attention, and depth-wise separable convolution modules are employed to enhance the feature quality and density with fewer parameters for the phases above. The experimental results on MSTAR and OpenSARship showed that the proposed HDLM performs better than the existing methods under the limited training samples.

A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR

It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN mainly use the image features of SAR images and make little use of the unique electromagnetic scattering characteristics of SAR images. For SAR images, attributed scattering centers (ASCs) reflect the electromagnetic scattering characteristics and the local structures of the target, which are useful for SAR ATR. Therefore, we propose a network to comprehensively use the image features and the features related to ASCs for improving the performance of SAR ATR. There are two branches in the proposed network, one extracts the more discriminative image features from the input SAR image; the other extracts physically meaningful features from the ASC schematic map that reflects the local structure of the target corresponding to each ASC. Finally, the high-level features obtained by the two branches are fused to recognize the target. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the capability of the SAR ATR method proposed in this letter.

FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

Automatic target recognition (ATR) for military applications is one of the core processes toward enhancing intelligence and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this article surveys and assesses current SAR ATR algorithms that employ the most popular dataset for the SAR domain, namely the moving and stationary target acquisition and recognition (MSTAR) dataset. Specifically, we perform a direct comparison between current SAR ATR methods and highlight the strengths and weaknesses of each technique under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset, we also highlight its weaknesses and suggest future research directions.

SAR Target Recognition via Meta-Learning and Amortized Variational Inference.

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural network (CNN)-based SAR ATR methods. However, the performance of CNN significantly deteriorates when the labeled samples are insufficient. To effectively utilize the unlabeled samples, we present a novel semi-supervised CNN method. In the training process of our method, the information contained in the unlabeled samples is integrated into the loss function of CNN. Specifically, we first utilize CNN to obtain the class probabilities of the unlabeled samples. Thresholding processing is performed to optimize the class probabilities so that the reliability of the unlabeled samples is improved. Afterward, the optimized class probabilities are used to calculate the scatter matrices of the linear discriminant analysis (LDA) method. Finally, the loss function of CNN is modified by the scatter matrices. We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of our method is significantly higher than other semi-supervised methods. It has been proved that our method can effectively improve the SAR ATR accuracy when labeled samples are insufficient.

SAR Target Recognition Based on Cross-Domain and Cross-Task Transfer Learning

Inspired by their tremendous success in optical image detection and classification, convolutional neural networks (CNNs) have recently been used in synthetic aperture radar automatic target recognition (SAR-ATR). Although CNN-based methods can achieve excellent recognition performance, it is difficult to collect a large number of real SAR images available for training. In this paper, we introduce simulated SAR data to alleviate the problem of insufficient training data. To address domain shift and task transfer problems caused by differences between simulated and real data, we propose a model that integrates meta-learning and adversarial domain adaptation. We use sufficient simulated data and a few real data to pre-train the model. After fine-tuning, the pre-trained model can quickly adapt to new tasks in real data. Extensive experimental results obtained in the real SAR dataset demonstrate that our model effectively solves the cross-domain and cross-task transfer problem. Compared with conventional SAR-ATR methods, the proposed model can achieve better recognition performance with a small amount of training data.

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

In this paper, a robust synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed by combining the global and local filters, especially aiming to improve the recognition performance under various extended operating conditions (EOCs). Due to the cleanness of the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, which is the prevalent benchmark for SAR ATR, extremely high performance has been reported in many literatures. However, in the real-world scenarios, there exist many types of EOCs such as noise corruption, partial occlusion, sensor variation, etc. Therefore, this study focuses on the SAR ATR problem under several typical EOCs. The intensities of the test image to be classified is taken as the global filter. The spatial correlation between the test image and its corresponding template is calculated as the global response, which describes the global consistency. To depict the local properties of the target, the attributed scattering centers (ASCs) are employed as local filters. Each ASC of the test image is matched with its counterpart from the template ASC set. Then, the local response is evaluated based on the attributes’ differences. The global response and local responses are linearly combined using a random weight matrix. Afterwards, a judgment variable is designed as the measure for target recognition. Experiments are conducted on the MSTAR dataset under the standard operating condition (SOC) and various EOCs including configuration variance, depression angle variance, noise corruption, resolution variance, and partial occlusion. Compared with several state-of-the-art SAR ATR methods, the validity and robustness of the proposed method is fully demonstrated.

An Efficient and Robust Framework for SAR Target Recognition by Hierarchically Fusing Global and Local Features.

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.

A Region Matching Approach Based on 3-D Scattering Center Model With Application to SAR Target Recognition

This paper proposes a region matching approach based on 3-D scattering center model with application to synthetic aperture radar (SAR) automatic target recognition (ATR). Each of the model scattering centers is represented by a binary region obtained by segmenting its predicted image using a global threshold. Afterwards, a region-to-region matching algorithm is performed to match the individual predicted regions with the extracted target region from the test image. Three scores are designed to comprehensively evaluate the matching result, which are linearly fused as an overall similarity based on their sparabilities as for discriminating different targets. In the classification stage, a coarse-to-fine region matching is performed and the test image is decided to be the class with the maximum similarity. Experiments are conducted on the simulated data by the electromagnetic code. The results demonstrate that the proposed method outperforms several template-based and model-based SAR ATR methods under the standard operating condition. The superiority of the proposed method becomes more remarkable under various extended operating conditions, including noise corruption, resolution variance, and partial occlusion.

Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR

This paper proposes a synthetic aperture radar (SAR) automatic target recognition (ATR) method via hierarchical fusion of two classification schemes, i.e., convolutional neural networks (CNN) and attributed scattering center (ASC) matching. CNN can work with notably high effectiveness under the standard operating condition (SOC). However, it can hardly cope with various extended operating conditions (EOCs), which are not covered by the training samples. In contrast, the ASC matching can handle many EOCs related to the local variations of the target by building a one-to-one correspondence between two ASC sets. Therefore, it is promising that both effectiveness and efficiency of the ATR method can be improved by combining the merits of the two classification schemes. The test sample is first classified by CNN. A reliability level calculated based on the outputs from CNN. Once there is a notably reliable decision, the whole recognition process terminates. Otherwise, the test sample will be further identified by ASC matching. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under SOC and various EOCs. The results demonstrate the superior effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR methods.