SAR Target Recognition Based on Joint Representation of Multimode Representations

Youchun Qiu,Bai Yuan Ding

doi:10.1155/2021/6153831

Youchun Qiu, Bai Yuan Ding

Open Access

https://doi.org/10.1155/2021/6153831

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Abstract

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).

Highlights

RSynthetic aperture radar (SAR) can work in all weather conditions to obtain high-resolution images that can be used for interpretation [1]. e existing synthetic aperture radar (SAR) target recognition methods are mainly improved or innovated from the two key steps of feature extraction and classification in order to improve the final recognition performance
Ding et al applied binary morphological operations in region matching and defined a robust similarity measure. e target outlines were used as basic features to evaluate the similarities between different SAR images with appellation to target recognition in [6, 7]. e principal component analysis (PCA), kernel PCA (KPCA), monogenic signal, bidimensional empirical mode decomposition (BEMD), and multiresolution representations were employed to develop SAR target recognition algorithms [8,9,10,11,12,13,14,15]
The corresponding decision-making mechanism is mainly designed by using mature classifiers or according to the characteristics of the features, including K-nearest neighbor (KNN) [8], support vector machine (SVM) [19,20,21], sparse representation-based classification (SRC) [21,22,23,24,25,26], and convolutional neural network (CNN) [27,28,29,30,31,32,33,34,35,36,37,38,39,40]

Summary

Introduction

RSynthetic aperture radar (SAR) can work in all weather conditions to obtain high-resolution images that can be used for interpretation [1]. e existing SAR target recognition methods are mainly improved or innovated from the two key steps of feature extraction and classification in order to improve the final recognition performance. E existing SAR target recognition methods are mainly improved or innovated from the two key steps of feature extraction and classification in order to improve the final recognition performance. Feature extraction aims to obtain de-redundant, low-dimensional representations of the original SAR images. In [2], the Zernike moments were adopted as regional features for SAR target recognition. E target outlines were used as basic features to evaluate the similarities between different SAR images with appellation to target recognition in [6, 7]. E principal component analysis (PCA), kernel PCA (KPCA), monogenic signal, bidimensional empirical mode decomposition (BEMD), and multiresolution representations were employed to develop SAR target recognition algorithms [8,9,10,11,12,13,14,15]. The corresponding decision-making mechanism is mainly designed by using mature classifiers or according to the characteristics of the features, including K-nearest neighbor (KNN) [8], support vector machine (SVM) [19,20,21], sparse representation-based classification (SRC) [21,22,23,24,25,26], and convolutional neural network (CNN) [27,28,29,30,31,32,33,34,35,36,37,38,39,40]

Methods

Results

Conclusion