高分三号NSC模式SAR图像舰船目标检测初探

Ship Detection in GF-3 NSC Mode SAR Images

高分三号卫星是我国首颗分辨率达到1m的C波段多极化合成孔径雷达(Synthetic Aperture Radar,SAR)卫星,拥有多种成像模式。该文针对高分三号NSC模式SAR图像提出一种海上舰船目标检测方法,其核心为基于贝叶斯框架的像素分类以实现目标筛选,并根据数据特点设计有效的图像降质条件下的性能提升方法。该文提出的检测算法与多种恒虚警率(Constant False Alarm Rate,CFAR)检测算法进行对比实验分析,实验结果证明了该文所提方法的有效性与性能优势。

GF-3, the first C-band full-polarimetric Synthetic Aperture Radar（SAR） satellite with a space resolution up to 1 m, has multiple strip and scan imaging modes. In this paper, we propose a maritime ship detection algorithm that detects ship targets via pixel classification in a Bayesian framework and employ effective enhancement methods to improve detection performance based on the data characteristics. We compare and analyze the results of detection experiments using the proposed algorithm with those of several Constant False Alarm Rate（CFAR） algorithms. The experimental results verify the effectiveness of the proposed algorithm.