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.

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Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

Remote Sensing ◽

10.3390/rs13081455 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1455

Author(s):

Jifang Pei ◽

Weibo Huo ◽

Chenwei Wang ◽

Yulin Huang ◽

Yin Zhang ◽

...

Keyword(s):

Target Recognition ◽

Feature Learning ◽

Automatic Target Recognition ◽

Operating Conditions ◽

Sar Images ◽

Single View ◽

Learning Network ◽

Deep Feature ◽

Deep Feature Learning ◽

Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

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Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

Scientific Programming ◽

10.1155/2016/3801053 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Hongqiao Wang ◽

Yanning Cai ◽

Guangyuan Fu ◽

Shicheng Wang

Keyword(s):

Target Recognition ◽

Low Frequency ◽

Automatic Target Recognition ◽

Recognition Algorithm ◽

Scale Transformation ◽

Sar Image ◽

Sar Images ◽

Stationary Target ◽

Multilevel Sampling ◽

Process Method

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

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Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

International Journal of Optics ◽

10.1155/2020/5464010 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Chenyu Li ◽

Guohua Liu

Keyword(s):

Bayesian Learning ◽

Block Structure ◽

Target Recognition ◽

Recognition Performance ◽

Test Sample ◽

Synthetic Aperture ◽

Sparse Bayesian Learning ◽

Sar Images ◽

Stationary Target ◽

Sar Target Recognition

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

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D-ATR for SAR Images Based on Deep Neural Networks

Remote Sensing ◽

10.3390/rs11080906 ◽

2019 ◽

Vol 11 (8) ◽

pp. 906 ◽

Cited By ~ 3

Author(s):

Zongyong Cui ◽

Cui Tang ◽

Zongjie Cao ◽

Nengyuan Liu

Keyword(s):

Neural Network ◽

Processing Speed ◽

Deep Neural Networks ◽

Accurate Result ◽

Target Recognition ◽

Recognition Performance ◽

Automatic Target Recognition ◽

Sar Images ◽

Sliding Method ◽

Fast Processing

Automatic target recognition (ATR) can obtain important information for target surveillance from Synthetic Aperture Radar (SAR) images. Thus, a direct automatic target recognition (D-ATR) method, based on a deep neural network (DNN), is proposed in this paper. To recognize targets in large-scene SAR images, the traditional methods of SAR ATR are comprised of four major steps: detection, discrimination, feature extraction, and classification. However, the recognition performance is sensitive to each step, as the processing result from each step will affect the following step. Meanwhile, these processes are independent, which means that there is still room for processing speed improvement. The proposed D-ATR method can integrate these steps as a whole system and directly recognize targets in large-scene SAR images, by encapsulating all of the computation in a single deep convolutional neural network (DCNN). Before the DCNN, a fast sliding method is proposed to partition the large image into sub-images, to avoid information loss when resizing the input images, and to avoid the target being divided into several parts. After the DCNN, non-maximum suppression between sub-images (NMSS) is performed on the results of the sub-images, to obtain an accurate result of the large-scene SAR image. Experiments on the MSTAR dataset and large-scene SAR images (with resolution 1478 × 1784) show that the proposed method can obtain a high accuracy and fast processing speed, and out-performs other methods, such as CFAR+SVM, Region-based CNN, and YOLOv2.

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Discrimination and Correlation Analysis of Multiview SAR Images with Application to Target Recognition

Scientific Programming ◽

10.1155/2021/6646388 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Lin Chen ◽

Peng Zhan ◽

Luhui Cao ◽

Xueqing Li

Keyword(s):

Correlation Analysis ◽

Target Recognition ◽

Recognition Performance ◽

Superior Performance ◽

Support Vector ◽

Sar Images ◽

Stationary Target ◽

Weighting Strategy ◽

Set Up ◽

Sar Target Recognition

A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.

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Combination of ELM and BEMD for Target Recognition of SAR Images

Journal of Circuits System and Computers ◽

10.1142/s0218126621501486 ◽

2021 ◽

Vol 30 (13) ◽

Author(s):

Zhichao Liu ◽

Baida Qu

Keyword(s):

Feature Extraction ◽

Embedded System ◽

High Efficiency ◽

Target Recognition ◽

Recognition Performance ◽

Recognition Algorithm ◽

Operating Conditions ◽

Intrinsic Mode Functions ◽

Sar Images ◽

Stationary Target

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

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An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

Remote Sensing ◽

10.3390/rs13193864 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3864

Author(s):

Changjie Cao ◽

Zongyong Cui ◽

Zongjie Cao ◽

Liying Wang ◽

Jianyu Yang

Keyword(s):

Target Recognition ◽

Recognition Performance ◽

Recognition Rate ◽

Automatic Target Recognition ◽

Small Sample ◽

Generative Adversarial Networks ◽

Training Set ◽

Stationary Target ◽

Sample Set ◽

Filtering Approach

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

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SAR ATR of Ground Vehicles Based on ESENet

Remote Sensing ◽

10.3390/rs11111316 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1316 ◽

Cited By ~ 10

Author(s):

Li Wang ◽

Xueru Bai ◽

Feng Zhou

Keyword(s):

Target Recognition ◽

Recognition Rate ◽

Automatic Target Recognition ◽

Synthetic Aperture ◽

Ground Vehicles ◽

Feature Maps ◽

Sar Images ◽

Angle Variation ◽

Stationary Target ◽

Depression Angle

In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms that are based on the convolutional neural network (CNN) have achieved high recognition rates in the moving and stationary target acquisition and recognition (MSTAR) dataset. However, in a SAR ATR task, the feature maps with little information automatically learned by CNN will disturb the classifier. We design a new enhanced squeeze and excitation (enhanced-SE) module to solve this problem, and then propose a new SAR ATR network, i.e., the enhanced squeeze and excitation network (ESENet). When compared to the available CNN structures that are designed for SAR ATR, the ESENet can extract more effective features from SAR images and obtain better generalization performance. In the MSTAR dataset containing pure targets, the proposed method achieves a recognition rate of 97.32% and it exceeds the available CNN-based SAR ATR algorithms. Additionally, it has shown robustness to large depression angle variation, configuration variants, and version variants.

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Multi-Block Mixed Sample Semi-Supervised Learning for SAR Target Recognition

Remote Sensing ◽

10.3390/rs13030361 ◽

2021 ◽

Vol 13 (3) ◽

pp. 361

Author(s):

Ye Tian ◽

Jianguo Sun ◽

Pengyuan Qi ◽

Guisheng Yin ◽

Liguo Zhang

Keyword(s):

Supervised Learning ◽

Target Recognition ◽

Recognition Performance ◽

Automatic Target Recognition ◽

Training Image ◽

Data Sets ◽

Data Set ◽

Stationary Target ◽

Mixing Method ◽

Sar Target Recognition

In recent years, synthetic aperture radar (SAR) automatic target recognition has played a crucial role in multiple fields and has received widespread attention. Compared with optical image recognition with massive annotation data, lacking sufficient labeled images limits the performance of the SAR automatic target recognition (ATR) method based on deep learning. It is expensive and time-consuming to annotate the targets for SAR images, while it is difficult for unsupervised SAR target recognition to meet the actual needs. In this situation, we propose a semi-supervised sample mixing method for SAR target recognition, named multi-block mixed (MBM), which can effectively utilize the unlabeled samples. During the data preprocessing stage, a multi-block mixed method is used to interpolate a small part of the training image to generate new samples. Then, the new samples are used to improve the recognition accuracy of the model. To verify the effectiveness of the proposed method, experiments are carried out on the moving and stationary target acquisition and recognition (MSTAR) data set. The experimental results fully demonstrate that the proposed MBM semi-supervised learning method can effectively address the problem of annotation insufficiency in SAR data sets and can learn valuable information from unlabeled samples, thereby improving the recognition performance.

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Multi-Stream Convolutional Neural Network for SAR Automatic Target Recognition

Remote Sensing ◽

10.3390/rs10091473 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1473 ◽

Cited By ~ 16

Author(s):

Pengfei Zhao ◽

Kai Liu ◽

Hao Zou ◽

Xiantong Zhen

Keyword(s):

Feature Fusion ◽

Target Recognition ◽

Recognition Performance ◽

Image Data ◽

Automatic Target Recognition ◽

Multiple Views ◽

Sar Images ◽

Operating Condition ◽

High Dependency ◽

Highly Nonlinear

Despite the fact that automatic target recognition (ATR) in Synthetic aperture radar (SAR) images has been extensively researched due to its practical use in both military and civil applications, it remains an unsolved problem. The major challenges of ATR in SAR stem from severe data scarcity and great variation of SAR images. Recent work started to adopt convolutional neural networks (CNNs), which, however, remain unable to handle the aforementioned challenges due to their high dependency on large quantities of data. In this paper, we propose a novel deep convolutional learning architecture, called Multi-Stream CNN (MS-CNN), for ATR in SAR by leveraging SAR images from multiple views. Specifically, we deploy a multi-input architecture that fuses information from multiple views of the same target in different aspects; therefore, the elaborated multi-view design of MS-CNN enables it to make full use of limited SAR image data to improve recognition performance. We design a Fourier feature fusion framework derived from kernel approximation based on random Fourier features which allows us to unravel the highly nonlinear relationship between images and classes. More importantly, MS-CNN is qualified with the desired characteristic of easy and quick manoeuvrability in real SAR ATR scenarios, because it only needs to acquire real-time GPS information from airborne SAR to calculate aspect differences used for constructing testing samples. The effectiveness and generalization ability of MS-CNN have been demonstrated by extensive experiments under both the Standard Operating Condition (SOC) and Extended Operating Condition (EOC) on the MSTAR dataset. Experimental results have shown that our proposed MS-CNN can achieve high recognition rates and outperform other state-of-the-art ATR methods.

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