We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multi-Aspect SAR Target Recognition Based on Non-Local and Contrastive Learning.
- Authors
Zhou, Xiao; Li, Siyuan; Pan, Zongxu; Zhou, Guangyao; Hu, Yuxin
- 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.
- Subjects
AUTOMATIC target recognition; SYNTHETIC aperture radar; FEATURE extraction; SPACE-based radar
- Publication
Mathematics (2227-7390), 2023, Vol 11, Issue 12, p2690
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math11122690