上海交通大学学报

上海交通大学学报 >

2020 , Vol. 54 >Issue 12: 1259 - 1268

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2019.311

合成孔径雷达图像舰船尾迹检测算法

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  • 1.上海交通大学　自动化系；系统控制与信息处理教育部重点实验室；海洋智能装备与系统教育部实验室，上海 　200240
    2.上海卫星工程研究所，上海 　201109
赵婷(1995-)，女，山西省运城市人，硕士生，从事图像处理等研究．

收稿日期: 2019-10-29

  网络出版日期: 2020-12-31

基金资助

国家科技重大专项(2018YFB1305003);国家自然科学基金(61627810);上海航天科技创新基金(SAST2015007);航天先进技术联合研究中心基金(USCAST2015-11);中国先进航空航天制造技术研究联合基金(USCAST2016-2)

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Detection Algorithm of Ship Wake in SAR Images

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  • 1.Department of Automation; Key Laboratory of System Control and Information Processing of the Ministry of Education; Key Laboratory of Marine Intelligent Equipment and System of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
    2.Shanghai Institute of Satellite Engineering, Shanghai 201109, China

Received date: 2019-10-29

  Online published: 2020-12-31

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摘要

针对合成孔径雷达(SAR)图像舰船尾迹检测问题，提出了基于解析字典的相干斑噪声抑制算法和基于Radon变换的尾迹检测算法．首先，利用基于解析字典的形态成分分离算法对SAR图像进行成分分离，得到含有舰船尾迹的结构成分图像和含有相干斑噪声及海杂波的纹理成分图像．然后，对包含舰船尾迹的结构成分进行局部Radon变换，并通过基于峰值聚类决策的舰船尾迹识别算法对真假局部峰值点进行判别，得到真实尾迹产生的局部峰值点，确定舰船尾迹的具体位置．实验结果表明，该算法能有效地完成SAR图像舰船尾迹检测．

关键词： 合成孔径雷达图像; 舰船尾迹; 乘性噪声去噪; 稀疏表示; Radon变换

本文引用格式

赵婷, 王申涛, 牛林, 席沛丽, 蔡云泽 . 合成孔径雷达图像舰船尾迹检测算法[J]. 上海交通大学学报, 2020 , 54(12) : 1259 -1268 . DOI: 10.16183/j.cnki.jsjtu.2019.311

Abstract

In order to solve the problem of ship wake detection in synthetic aperture radar (SAR) image, a coherent speckle noise suppression algorithm based on analytic dictionary and a wake detection algorithm based on Radon transform are proposed. First, the morphological component separation algorithm based on analytic dictionary is used to separate the components of SAR image. The structural component image with ship wake and the texture component image with speckle noise and sea clutter are obtained. Then, the local Radon transform is performed on the structural component that contains the ship wakes. Finally, the real and false local peak points are distinguished by the ship wake recognition algorithm based on peak clustering decision, so as to obtain the local peak points generated by the real wakes and determine the specific position of the ship wakes. The experimental results show that the proposed algorithm can effectively detect the ship wakes in SAR images.

Key words： synthetic aperture radar (SAR) image; ship wake; multiplicative noise denoising; sparse representation; Radon transform

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