Target Recognition of SAR Images Based on SVM and KSRC.

Haiyan Zhao

doi:10.1155/2021/4322678

Abstract

A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original SAR image, respectively, which are classical and reliable feature extraction algorithms. In addition, KPCA can effectively make up for the weak linear description ability of PCA. Afterwards, support vector machine (SVM) and kernel sparse representation-based classification (KSRC) are used to classify the KPCA and PCA feature vectors, respectively. Similar to the idea of feature extraction, KSRC mainly introduces kernel functions to improve the processing and classification capabilities of nonlinear data. Through the combination of linear and nonlinear features and classifiers, the internal data structure of SAR images and the correspondence between test and training samples can be better investigated. In the experiment, the performance of the proposed method is tested based on the MSTAR dataset. The results show the effectiveness and robustness of the proposed method.

Highlights

Synthetic aperture radar (SAR) can realize all-day and allweather reconnaissance through high-resolution remote imaging. e intelligent interpretation of massive SAR images has become a research focus
Data analysis algorithms represented by principal component analysis (PCA) and linear discriminant analysis (LDA) [7, 8] have been widely used in SAR image feature extraction and target recognition
The horizontal and vertical coordinates correspond to the actual target category and the target category predicted by the proposed method, respectively. erefore, the elements on the diagonal are the correct recognition rates of various targets

Summary

Introduction

Synthetic aperture radar (SAR) can realize all-day and allweather reconnaissance through high-resolution remote imaging. e intelligent interpretation of massive SAR images has become a research focus. SAR target recognition aims to confirm the category of the target of interest in the SAR image, mainly by combining feature extraction and classifier [1]. Feature extraction is employed to achieve dimensionality reduction and compression of high-dimensional SAR images, thereby improving the efficiency and accuracy of subsequent classification. Data analysis algorithms represented by principal component analysis (PCA) and linear discriminant analysis (LDA) [7, 8] have been widely used in SAR image feature extraction and target recognition. Their effectiveness has been verified by experiments. With the popularity of manifold learning algorithms [9,10,11], new feature extraction methods such as nonnegative matrix factorization (NMF) [9] further improved the target classification performance

Methods

Results

Conclusion