Vision-Inspired Filtering Algorithm for SAR Ship Detection Based on Generative Adversarial Networks

Ship detection in synthetic aperture radar (SAR) images has been widely applied in the military and civil fields. However, the background environment of SAR images is complex and there are many interferences similar to the ship targets, which is easy to lead fault detection and affect the detection performance. To address this problem, a vision-inspired filtering algorithm (FilterGAN) is proposed to filter out the target-irrelevant information. First, we build a representation model based on filtering mechanism of human brain to guide the design of filtering network. Second, to simulate the adjustment process of the priority map reconstruction in human brain, generative adversarial networks (GAN) are used to learn the optimal filtering mapping function. To train FilterGAN, we introduce the labeling process to generate the ground-truth filtered SAR image. Experimental results on AIR-SARShip-1.0 dataset demonstrate that the detection performance of SAR ships can be improved obviously with FilterGAN.