PHYSICS-DRIVEN INTERPRETABLE SAR TARGET RECOGNITION NETWORK BASED ON SCATTERING CENTER FEATURE EXTRACTION

Synthetic aperture radar (SAR) target recognition methods based on deep learning have been a research hot-spot. But most of them are of black-box structure and neglect physical characteristics of SAR targets, which restrict the recognition performance. Scattering centers (SCs) are physical features that reflect structure and shape information of targets. Thus, this paper designs a physics-driven interpretable SAR target recognition network based on scattering center feature extraction. Incorporating the scattering center model of SAR target into network, our method is interpretable deep model that learns the SC features with specific physical meanings. Moreover, the learned SC features are then constructed as the SC geometric images, which are further projected into a designed target recognition network for target recognition. In particular, our model is an end-to-end model that achieves SCs feature extraction and target recognition in a framework, which can avoid the mismatch issue between SC features and classifier and ensure promising recognition performance. Experiments on the measured MSTAR dataset validate the superior performance of our method.