Occluded SAR Target Recognition Based on Center Local Constraint Shadow Residual Network

Abstract

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely used by scholars around the world and achieved excellent results. However, occluded SAR target recognition is still a very challenging task. In this letter, we propose a center local constraint shadow residual network (ClcsrNet) for occluded SAR target recognition. First, the shadow features of SAR images are extracted to improve the robustness of the network to occlusion. Then, the shadow features, the target convolutional features, and the residual features are fused to increase the feature diversity of the network. Finally, we combine the center loss and the local constraint loss to optimize the network. The center loss is used to better cluster the targets in the same class. The local constraint loss is used to maintain the local structure of the target, which increases the separability between different classes. Experiments on the moving and stationary target acquisition and recognition (MSTAR) datasets demonstrate that the proposed ClcsrNet can achieve higher accuracy and better robustness than the comparison algorithms in occluded SAR target recognition.