Enhanced Mask Interaction Network for SAR Ship Instance Segmentation

Abstract

We propose an enhanced mask interaction network (EMIN) for ship instance segmentation from synthetic aperture radar (SAR) images. EMIN adopts three techniques to improve SAR ship instance segmentation performance — 1) an atrous spatial pyramid pooling (ASPP) to enable multi-resolution feature responses, 2) a non-local block (NLB) to capture long-range spatial dependencies, and 3) a concatenation shuffle attention (CSA) to boost mask interaction benefits. Results on the public SAR ship detection dataset (SSDD) show that — 1) the above each technique can offer an observable accuracy gain, and 2) EMIN surpasses the original MIN by 2.1% detection AP and 2.4% mask AP on SSDD.