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一种面向多星多分辨率的SAR图像舰船候选区域提取方法 被引量:1

Candidate Region Extraction Method for Multi-satellite and Multi-resolution SAR Ships
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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。 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 multiresolution 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.
作者 胡炎 单子力 高峰 HU Yan;SHAN Zili;GAO Feng(CETC Key Laboratory of Aerospace Information Applications,Shijiazhuang 050081,China)
出处 《电子与信息学报》 EI CSCD 北大核心 2019年第4期770-778,共9页 Journal of Electronics & Information Technology
基金 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)~~
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 Image processing Ship candidate region Mean dichotomy Target reconstruction Seed point growth Threshold adaptive
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