A Novel Method Combining Global Visual Features and Local Structural Features for SAR ATR

作     者：Li, Chen Du, Lan Li, Yi 

作者机构：Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China 

出 版 物：《IEEE GEOSCIENCE AND REMOTE SENSING LETTERS》 (IEEE Geosci. Remote Sens. Lett.)

年 卷 期：2023年第20卷

页      面：1页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Science Foundation of China [U21B2039] 

主　　题：Feature extraction Visualization Synthetic aperture radar Convolutional neural networks Data mining Radar polarimetry Image reconstruction Attributed scattering center (ASC) convolutional neural network (CNN) graph convolutional network (GCN) synthetic aperture radar (SAR) target recognition 

摘      要：The mainstream synthetic aperture radar (SAR) automatic target recognition (ATR) methods commonly use convolutional neural network (CNN) to extract the visual information of SAR targets, while the physical structural information is seldom considered. Scattering center features can describe the targets physical structure information and are robust to the local variations of targets, which can be exploited to reflect the local structural characteristic of SAR targets. Therefore, we propose a novel method that effectively combines global visual features and local structural features for SAR ATR. The local structural features here contain not only the local physical structure information but also the local visual information. Our proposed method consists of three parts: global-based module, local-based module, and feature fusion module. Global-based module utilizes CNN to extract global visual features from SAR images. Local-based module first extracts attributed scattering centers (ASCs) from the complex SAR image and models each ASC as a node to construct graph data, from which we further use a multiscale graph convolutional network (GCN) to extract local structural features. The node features for GCN learning are constructed by multiplying the corresponding local visual features in shallow CNN feature maps with the ASC reconstruction maps to better reflect the local structure characteristic. Then, the learned local structural features from GCN are further fused with global visual features to achieve SAR ATR. As far as authors know, this is the first work combining CNN and GCN to effectively extract global visual features and local structural features simultaneously in SAR ATR. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset show that our proposed method outperforms SOTA methods in terms of classification accuracy.