Cross-Domain Transfer for Ship Instance Segmentation in SAR Images

Abstract

Considering insufficient data and difficulty of labeling in Synthetic Aperture Radar (SAR) images, we propose a method for SAR ship instance segmentation based on cross-domain transfer learning. Compared with optical images, transfer learning in SAR images faces the difficulties of insufficient data to pre-train and lacking detail features. The proposed method, containing sample transfer module and knowledge transfer module, simulates images from optics to SAR and pre-train the ship detection part of the instance segmentation network with simulation images. In addition, we design a Res-Pyramid network to prevent the deep network from being unable to extract efficient features of SAR images. The method proposed combines the content of the optics and the style of the SAR and incorporates multiscale features in backbone, which improves performance in ship instance segmentation in SAR images. Experiments show that it has achieved 1.3 and 1.1 points higher Average Precision (AP) in detection and segmentation tasks on SAR dataset of HRSID when using cross-domain transfer learning, which has exceeded state-of-the-art methods.