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

• Main Authors: Jifang Pei, Zhiyong Wang, Xueping Sun, Weibo Huo, Yin Zhang, Yulin Huang, Junjie Wu, Jianyu Yang
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Remote Sensing
• Subjects: synthetic aperture radar; multiview; automatic target recognition; deep neural network; feature extraction; feature fusion
• Online Access: https://www.mdpi.com/2072-4292/13/17/3493

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.