单子力,,
高峰
中国电子科技集团公司航天信息应用技术重点实验室 ??石家庄 ??050081
基金项目:中国电子科技集团公司航天信息应用技术重点实验室开放基金(EX166290025)
详细信息
作者简介:胡炎：男，1991年生，硕士，工程师，研究方向为图像智能处理技术与机器学习
单子力：男，1980年生，博士，高级工程师，研究方向为微波遥感与大系统集成技术
高峰：男，1978年生，学士，高级工程师，研究方向为航天应用总体设计
通讯作者:单子力 huyantju@126.com
中图分类号:TN957.52计量
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出版历程
收稿日期:2018-05-29
修回日期:2018-12-18
网络出版日期:2018-12-26
刊出日期:2019-04-01
Candidate Region Extraction Method for Multi-satellite and Multi-resolution SAR Ships
Yan HU,Zili SHAN,,
Feng GAO
CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China
Funds:The Open Foundation of CETC Key Laboratory of Aerospace Information Applications (EX166290025)
摘要
摘要:基于CFAR和核密度估计(KDE)的SAR传统舰船候选区域提取方法存在以下缺陷：CFAR虚警率依赖人工经验选择；CFAR仅对杂波分布建模，会对被检目标构成一定的漏检风险；利用KDE进行强海杂波过滤时，需凭人工经验选择滤除阈值。这使得传统舰船候选区域提取方法无法适应多星多分辨率等复杂场景。该文提出一种面向多星多分辨率的SAR图像舰船候选区域提取算法，针对CFAR算法的缺陷，提出采用均值二分法迭代逼近目标计算分割阈值，在克服CFAR缺陷的同时，计算效率比CFAR提高10倍以上；针对KDE的缺陷，提出了区块KDE结合大阈值滤除强海杂波，再借助种子点生长算法重建目标。由于大阈值具有足够的阈量，使得算法可以适应更复杂的场景。实验表明所提方法具有不漏检、阈值自适应、计算效率高、虚警率低的优点，具备优秀的多星多分辨率SAR舰船候选区域提取能力。
关键词:图像处理/
舰船候选区域/
均值二分法/
目标重建/
种子点生长/
阈值自适应
Abstract:The traditional methods based on CFAR and Kernel Density Estimation (KDE) for SAR ship candidate region extraction has the following defects: The choice of false alarm rate of CFAR depends on artificial experience; CFAR only models the sea clutter distribution, which poses a certain risk of missing detection to the target; When KDE is used to filter strong sea clutter, the threshold must be selected by artificial experience. These defects make the traditional method unable to adapt to complex scene, such as multi-satellite and multi-resolution. A candidate region extraction method for multi-satellite and multi-resolution SAR ships is proposed. In view of the defects of CFAR, an iterative method of mean dichotomy is proposed to approximate the target and calculate the segmentation threshold. The calculation efficiency of this method is more than 10 times higher than that of CFAR while overcoming the defects of CFAR; In view of the defects of KDE, block KDE combined with large threshold is used to filter strong sea clutter, and then seed point growth algorithm is used to reconstruct target. Because the large threshold has enough thresholds, the method can adapt to more complex scenarios. Experiments show that the proposed method has the advantages of no missed detection, self-adaptive threshold, high computational efficiency, and low false alarm rate. It has excellent multi-satellite and multi-resolution SAR ship candidate region extraction capability.
Key words:Image processing/
Ship candidate region/
Mean dichotomy/
Target reconstruction/
Seed point growth/
Threshold adaptive
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