GROUP-WISE SHUFFLE ATTENTION R-CNN FOR SHIP DETECTION IN DUAL-POLARIZATION SAR IMAGES

Ship detection in synthetic aperture radar (SAR) images is a hot pot. However, most existing convolution neural network (CNN)-based research is limited to single polarization ship detection and neglects the utilize of the rich polarization information to further improve detection performance. Thus, to address the problem, in this paper, a group-wise shuffle attention R-CNN (GWSA R-CNN) is proposed for ship detection in dual-polarization SAR images. Based on the raw Faster R-CNN, GWSA R-CNN embeds a group-wise shuffle attention module (GWSA module) in the detection subnetwork to capture enriched organic fusion polarization information. Finally, the experimental results on the dual-polarization SAR ship detection dataset (DSSDD) show the state-of-the-art (SOTA) performance of our GWSA R- CNN, outperforming than other 7 competitive models. Specifically, GWSA R-CNN surpasses the second-best model 1.82% average precision (AP).