Global and Local Context-Aware Ship Detector for High-Resolution SAR Images

Abstract

Due to the large sizes of synthetic aperture radar (SAR) images, traditional deep learning-based ship detection methods usually utilize the sliding window preprocessing strategy to obtain the small-sized sub-images. However, there are amounts of background clutter areas without ships in SAR images. Thus, traditional sliding window-based methods may generate numerous sub-images without ships, which can bring high computation redundancy and numerous false alarms. To deal with the above problems, in this paper, a novel detection method called global and local context-aware ship detector (GLC-Det) for high-resolution SAR images is proposed. The proposed method mainly contains three parts: the global context-aware based sub-images selection (GCSS) module, the deep learning module, and the local context-aware based false alarms suppression (LCFS) module. The GCSS module employs the global context information to eliminate the sub-images without ships, which can enhance detection efficiency and avoid generating numerous false alarms. The deep learning module is used to further obtain the preliminary detection boxes. The LCFS module is a postprocessing step, which employs the local context information around the detection boxes to further eliminate the false alarms. The experimental results on the measured data of AIR-SARShip-1.0 and 2.0 high-resolution SAR images demonstrate that the proposed method has higher precision and efficiency than the original deep learning methods.