MV-ResFPN: a residual network deeply combining multi-view and multiscale fusion for SAR target recognition

Zijun Wang; Gong Zhang; Daiyin Zhu; Qijun Dai

doi:10.1117/12.3057774

13 February 2025 MV-ResFPN: a residual network deeply combining multi-view and multiscale fusion for SAR target recognition

Zijun Wang, Gong Zhang, Daiyin Zhu, Qijun Dai

Author Affiliations +

Proceedings Volume 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024); 135390V (2025) https://doi.org/10.1117/12.3057774
Event: Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 2024, Nanjing, China

Abstract

Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has always had a problem, which is the large intra-class variance and small inter-class differences in the dataset due to angle sensitivity and differences in shallow and deep features. This article deeply combines multi-view methods with multiscale feature methods and proposes a Multi-View Residual Feature Pyramid Network (MV-ResFPN) for SAR target recognition. For multi-view targets, four residual blocks of ResNet50 are used to extract multi-scale feature information of the target from each view. Perform multi-view feature fusion on feature of the same scale to obtain four different scales of multi-view fusion features. Next, these four features are fed into the proposed adaptive Feature Pyramid Network (FPN) for multi-scale fusion, which adaptively fuses shallow to deep features and the final output of ResNet. The proposed method reduces intra-class differences and makes inter-class differences more pronounced. Experiments on the Bistatic CircularSAR dataset captured by our team have shown that this method outperforms existing techniques in SAR ATR.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zijun Wang, Gong Zhang, Daiyin Zhu, and Qijun Dai "MV-ResFPN: a residual network deeply combining multi-view and multiscale fusion for SAR target recognition", Proc. SPIE 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 135390V (13 February 2025); https://doi.org/10.1117/12.3057774

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