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

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.

scholarly journals A SAR Target Recognition Method via Combination of Multilevel Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhua Wang ◽  
Yuan Jiang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Recognition Method ◽  
Sar Images ◽  
Noise Interference ◽  
Feature Sets ◽  
Weighted Fusion ◽  
Standard Operating Condition ◽  
Sar Target Recognition

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
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.

scholarly journals Target Recognition in SAR Images Based on Multiresolution Representations with 2D Canonical Correlation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaojing Tan ◽  
Ming Zou ◽  
Xiqin He
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Operating Conditions ◽  
Data Set ◽  
Stationary Target ◽  
Sar Target Recognition

This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution representations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated to be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is beneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of the multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively convey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens caused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed to classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works. The moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method. According to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of targets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration variance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison with some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.

scholarly journals A Multiview SAR Target Recognition Method Using Inner Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lei Lei ◽  
Dongen Guo ◽  
Zhihui Feng
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Image Correlation ◽  
Multiple Views ◽  
Recognition Method ◽  
Sar Images ◽  
Weighting Method ◽  
Multiview Images ◽  
Correlation Information ◽  
Sar Target Recognition

This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.

scholarly journals Target Recognition of Synthetic Aperture Radar Images by Updated Classifiers

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jingyu Li ◽  
Cungen Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Original Training ◽  
Stationary Target ◽  
Radar Images ◽  
Training Samples ◽  
Sar Target Recognition ◽  
Aperture Radar

For the problem of reliable decision in synthetic aperture radar (SAR) target recognition, a method based on updated classifiers is proposed. The convolutional neural network (CNN) and support vector machine (SVM) are used as basic classifiers to classify samples with unknown target labels. The two decisions are fused and the reliability of the fused decision is evaluated. The classified test samples with high reliabilities are added to the original training samples to update the classifiers. The updated classifiers have stronger classification abilities and the fused result of the two classifiers can obtain a more reliable decision. The proposed method is tested and verified based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The experimental results verify the effectiveness and robustness of the proposed method.

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

2021 ◽  
Vol 13 (17) ◽  
pp. 3493
Author(s):  
Jifang Pei ◽  
Zhiyong Wang ◽  
Xueping Sun ◽  
Weibo Huo ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Imaging System ◽  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Sar Images ◽  
Stationary Target ◽  
Single View ◽  
Learning Modules ◽  
Deep Feature Extraction ◽  
Classification Information

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.

Combination of ELM and BEMD for Target Recognition of SAR Images

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.

scholarly journals SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

2019 ◽  
Vol 11 (22) ◽  
pp. 2676 ◽  
Author(s):  
Meiting Yu ◽  
Sinong Quan ◽  
Gangyao Kuang ◽  
Shaojie Ni
Keyword(s):  
Target Recognition ◽  
Operating Conditions ◽  
Monogenic Signal ◽  
Sar Images ◽  
Stationary Target ◽  
Common Information ◽  
Training Samples ◽  
New Strategy ◽  
Dense Representation ◽  
Sar Target Recognition

Synthetic aperture radar (SAR) target recognition under extended operating conditions (EOCs) is a challenging problem due to the complex application environment, especially for insufficient target variations and corrupted SAR images in the training samples. This paper proposes a new strategy to solve these problems for target recognition. The SAR images are firstly characterized by multi-scale components of monogenic signal. The generated monogenic features are decomposed to learn a class dictionary and a shared dictionary, which represent the possible intraclass variations information and the common information, respectively. Moreover, a sparse representation of the class dictionary and a dense representation of the shared dictionary are jointly employed to represent a query sample for classification. The validity of the proposed strategy is demonstrated with multiple comparative experiments on moving and stationary target acquisition and recognition (MSTAR) database.

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

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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