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

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.