Dark Ship Detection via Optical and SAR Collaboration: An Improved Multi-Feature Association Method Between Remote Sensing Images and AIS Data

作者：Fan Li, Kun Yu, Chao Yuan, Yichen Tian, Guang Yang, Kai Yin, Yongqiang Li · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17132201 · 被引用次数：13 · 研究领域：Infrared Target Detection Methodologies、Remote-Sensing Image Classification、Advanced Neural Network Applications

Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote sensing and AIS data, with a focus on oriented bounding box course estimation, to improve the detection of dark ships and enhance maritime surveillance. Firstly, the oriented bounding box object detection model (YOLOv11n-OBB) is trained to break through the limitations of horizontal bounding box orientation representation. Secondly, by integrating position, dimensions (length and width), and course characteristics, we devise a joint cost function to evaluate the combined significance of multiple features. Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. Finally, by integrating data from Gaofen-6 (optical) and Gaofen-3B (SAR) satellites, a day-and-night collaborative monitoring framework is constructed to address the blind spots of single-sensor monitoring during night-time or adverse weather conditions. Our results indicate that the detection model demonstrates a high average precision (AP50) of 0.986 on the optical dataset and 0.903 on the SAR dataset. The association accuracy of the multi-feature association algorithm is 91.74% in optical image and AIS data matching,...