Multi-Aspect SAR Target Recognition Based on Non-Local and Contrastive Learning

Xiao Zhou, Siyuan Li, Zongxu Pan, Guangyao Zhou () and Yuxin Hu
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Xiao Zhou: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Siyuan Li: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Zongxu Pan: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Guangyao Zhou: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yuxin Hu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Mathematics, 2023, vol. 11, issue 12, 1-17

Abstract: Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely applied in multiple fields. However, the special imaging mechanism of SAR results in different visual features of the same target at different azimuth angles, so single-aspect SAR target recognition has the limitation of observing the target from a single perspective. Multi-aspect SAR target recognition technology can overcome this limitation by utilizing information from different azimuths and effectively improve target recognition performance. Considering the order dependency and data limitation of existing methods, this paper proposes a multi-aspect SAR recognition method based on Non-Local, which applies a self-attention calculation to feature maps to learn the correlation between multi-aspect SAR images. Meanwhile, in order to improve the generalization ability of the proposed method under limited data, a network based on contrastive learning was designed to pre-train the feature extraction part of the whole network. The experimental results using the MSTAR dataset show that the proposed method has excellent recognition accuracy and good robustness.

Keywords: synthetic aperture radar (SAR); automatic target recognition (ATR); multiview; Non-Local; contrastive learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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