自动化学报2017，Vol.43Issue(10)：1713-1725,13.DOI:10.16383/j.aas.2017.c160274
基于形态字典学习的复杂背景SAR图像舰船尾迹检测
Ship Wake Detection in SAR Images with Complex Background Using Morphological Dictionary Learning
摘要
Abstract
Detection of ship wakes in SAR images is helpful not only in estimating the speed and the direction of moving ships,but also in finding small ship objects.The existing ship wake detection methods for SAR images can achieve satisfactory results only for simple background,but can hardly work for complex background.In this paper,a novel ship wake detection method for complex background based on morphological component analysis (MCA) and multi-dictionary learning.In this method,a SAR image is decomposed into a cartoon component containing ship wakes,and the process of the decomposition is supported by a ship wake dictionary built analytically and renewed iteratively.At the same time,the SAR image is also decomposed into a texture component supported by a sea-surface texture dictionary learnt off-line.Then,the cartoon component is enhanced by the shearlet transform and the high-frequency coefficient reconstruction.At last,the ship wake lines are detected from the enhanced cartoon component by Radon transform.Experimental results show that the performance of the proposed method outperforms other state-of-the-art methods for detection of ship wakes in SAR images with complex background.关键词
SAR图像/舰船尾迹检测/形态成分分析/字典学习/剪切波变换Key words
SAR image/ship wake detection/morphological component analysis (MCA)/dictionary learning/shearlet transform引用本文复制引用
杨国铮,禹晶,肖创柏,孙卫东..基于形态字典学习的复杂背景SAR图像舰船尾迹检测[J].自动化学报,2017,43(10):1713-1725,13.基金项目
国家自然科学基金(61501008),首都卫生发展科研专项(2014-2-4025)资助 （61501008）
Supported by National Natural Science Foundation of China(61501008),The Capital Health Research and Development of Special Funding (2014-2-4025) （61501008）
