Occluded Target Recognition in SAR Imagery With Scattering Excitation Learning and Channel Dropout

Deep neural networks (DNNs) are widely used in synthetic aperture radar (SAR) image classification and recognition, achieving state-of-the-art performance. But it remains a challenging task to recognize occluded targets. In this letter, we propose a novel robust SAR recognition method against occlusion. Specifically, we design a scattering excitation learning (SEL) module that encourages the network to learn more robust features responding to the scattering centers of targets. In addition, we adopt a random feature channel dropout technique which can further improve robustness to occlusion. Our method makes the network more robust against occlusion but without any occlusion-simulated data for training. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset show that our proposed method achieves remarkably improved robustness even under severe occlusions. The code is made available at https://github.com/koervcor/ SEL-CD.