Adaptive Scattering Feature Awareness and Fusion for Limited Training Data SAR Target Recognition

Deep learning methods have achieved huge success in synthetic aperture radar (SAR) target recognition, yet insufficient data limits their performances. If the key prior knowledge that describes the target is incorporated into the network, this problem can be effectively mitigated. Fortunately, electromagnetic scattering feature (ESF) elegantly and accurately models SAR targets, which describe the intrinsic properties of targets in SAR images. Therefore, how to perceive and integrate ESFs emerges as an essential issue. To address this problem, the adaptive scattering feature awareness and fusion network is proposed to obtain the more holistic target description. Specifically, the scattering structure intelligent awareness module is designed to automatically distinguish and screen the critical scattering information from the SAR image. Subsequently, in the feature fusion phase, the adaptive feature fusion module fuses the scattering domain ESFs and image domain deep features deeply and hierarchically by narrowing the distribution gap between them. In addition, a novel azimuth angle-guided classifier is employed to capture the association information between azimuth angle and target features. The physical laws inherent in ESF and azimuth angle guide the network training period, which improves feature discrimination and mitigates the pressure of data inadequacy. Experimental results on the moving and stationary target acquisition and recognition dataset and full aspect stationary targets-vehicle dataset demonstrate that adaptive scattering feature awareness and fusion network achieves state-of-the-art performance in SAR target recognition tasks.