Oriented Ship Detection Based on Strong Scattering Points Network in Large-Scale SAR Images

Abstract

Ship detection has broad applications in many areas, including fishery management, maritime rescue, and maritime monitoring. Recently, numerous detectors based on deep learning have been carried in ship detection in synthetic aperture radar (SAR) images. However, detecting the inshore ships faces enormous challenges because of the strong scattering interference of the inland area. In order to address such issues, a novel method named strong scattering points network for ship detection is proposed in this article. First, according to the SAR imaging mechanism, the ships usually appear strong scattering phenomenon in the SAR images. Therefore, the proposed method detects the strong scattering points on the ship and then aggregates their positions to obtain the ship’s arbitrary orientation box. Second, our method designs an embedding vector to cluster these points as an individual object to regress the oriented bounding box. Third, in order to distinguish the strong scattering points on land, a ship attention module is employed to extract the image texture features and representations of local features. It can suppress the false alarm caused by land interference in the detection process. Furthermore, to demonstrate the effectiveness of the proposed algorithm, this article introduces a new ship dataset for oriented ship detection named large-scale dataset for ship detection in SAR images (LDSD). Moreover, the public SAR ship detection dataset (SSDD) is utilized to verify the robustness and generalization ability of the detector. The experimental results on two datasets show that our method has a strong anti-interference ability in the inshore background and achieves state-of-the-art detection performance.