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FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition
| Content Provider | MDPI |
|---|---|
| Author | Pei, Jifang Wang, Zhiyong Sun, Xueping Huo, Weibo Zhang, Yin Huang, Yulin Wu, Junjie Yang, Jianyu |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 3493 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13173493 |
| Journal | Remote Sensing |
| Issue Number | 17 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-02 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Synthetic Aperture Radar Multiview Automatic Target Recognition Deep Neural Network Feature Extraction Feature Fusion |
| Content Type | Text |
| Resource Type | Article |
