融合极化旋转域特征和超像素技术的极化SAR舰船检测

崔兴超 粟毅 陈思伟

崔兴超, 粟毅, 陈思伟. 融合极化旋转域特征和超像素技术的极化SAR舰船检测[J]. 雷达学报, 2021, 10(1): 35–48. doi: 10.12000/JR20147
引用本文: 崔兴超, 粟毅, 陈思伟. 融合极化旋转域特征和超像素技术的极化SAR舰船检测[J]. 雷达学报, 2021, 10(1): 35–48. doi: 10.12000/JR20147
CUI Xingchao, SU Yi, and CHEN Siwei. Polarimetric SAR ship detection based on polarimetric rotation domain features and superpixel technique[J]. Journal of Radars, 2021, 10(1): 35–48. doi: 10.12000/JR20147
Citation: CUI Xingchao, SU Yi, and CHEN Siwei. Polarimetric SAR ship detection based on polarimetric rotation domain features and superpixel technique[J]. Journal of Radars, 2021, 10(1): 35–48. doi: 10.12000/JR20147

融合极化旋转域特征和超像素技术的极化SAR舰船检测

doi: 10.12000/JR20147
基金项目: 国家自然科学基金(61771480),湖南省自然科学基金(2020JJ2034),湖湘青年英才项目(2019RS2025),装备预研基金项目(61404160109),国防科技大学科研计划重点项目(ZK18-02-14)
详细信息
    作者简介:

    崔兴超(1994–),男,山东人,国防科技大学博士研究生。主要研究方向为极化SAR和目标检测。E-mail: nudt_cui@163.com

    粟毅:粟 毅(1961–),男,山东人,博士,国防科技大学教授,博士生导师。主要研究方向为微波成像、遥感应用、探地雷达、超宽带雷达系统等。E-mail: y.su@yeah.net

    陈思伟(1984–),男,四川人,博士,国防科技大学电子科学学院特聘教授,硕士生导师。主要研究方向为极化雷达成像、目标识别、电子对抗等。E-mail: chenswnudt@163.com

    通讯作者:

    陈思伟 chenswnudt@163.com

  • 责任主编:杨健 Corresponding Editor: YANG Jian
  • 中图分类号: TN958

Polarimetric SAR Ship Detection Based on Polarimetric Rotation Domain Features and Superpixel Technique

Funds: The National Natural Science Foundation of China (61771480), The Natural Science Foundation of Hunan Province (2020JJ2034), The Youth Talents Project of Hunan Province (2019RS2025), The Equipment Pre-Research Foundation (61404160109), The Key Research Projects of National University of Defense Technology (ZK18-02-14)
More Information
  • 摘要: 对海监视是极化SAR的重要应用,密集区域的舰船目标检测是当前面临的主要挑战之一。舰船密集区域受多目标串扰,传统的恒虚警率(CFAR)检测滑窗难以选取纯净的海杂波样本用于确定检测门限,将导致检测性能下降。针对这一问题,该文从特征提取和检测器设计两方面出发,提出一种融合极化旋转域特征和超像素技术的极化SAR舰船检测方法。在特征提取方面,雷达目标的后向散射敏感于目标姿态与雷达视线的相对几何关系,由此带来的散射多样性隐含信息可通过极化旋转域分析进行挖掘。该文利用极化相关方向图及导出的一系列极化旋转域特征,根据目标杂波比(TCR)分析,优选TCR最高的3个极化特征量用于构建目标检测器。在此基础上,该文在检测器设计方面提出了一种基于K均值聚类的杂波超像素筛选方法,有效避免了密集区域舰船目标对邻近杂波的影响,基于筛选的杂波像素点得到舰船目标CFAR检测结果。基于Radarsat-2和高分三号星载全极化SAR数据的对比实验表明,所提方法能有效实现密集区域舰船目标检测,检测品质因数达到95%。

     

  • 图  1  极化相关方向图可视化表征

    Figure  1.  Visualization of polarimetric correlation pattern

    图  2  极化特征目标杂波比

    Figure  2.  Target-to-Clutter Ratio of polarimetric features

    图  3  Radarsat-2数据及其超像素分割结果

    Figure  3.  Radarsat-2 data and its superpixel segmentation results

    图  4  融合极化旋转域特征和超像素技术的舰船检测方法流程图

    Figure  4.  Flowchart of ship detection method combing polarimetric rotation domain features and superpixel technique

    图  5  Radarsat-2数据

    Figure  5.  Radarsat-2 data

    图  6  高分三号数据I

    Figure  6.  GaoFen-3 data I

    图  7  高分三号数据II

    Figure  7.  GaoFen-3 data II

    图  8  Radarsat-2数据对比方法检测结果

    Figure  8.  Detection results of comparative methods with Radarsat-2 data

    图  9  高分三号数据I对比方法检测结果

    Figure  9.  Detection results of comparative methods with GaoFen-3 data I

    图  10  高分三号数据II对比方法检测结果

    Figure  10.  Detection results of comparative methods with GaoFen-3 data II

    表  1  Radarsat-2数据定量检测结果

    Table  1.   Quantitative detection results of Radarsat-2 data

    方法${N_{\rm{C}}}$${N_{\rm{M}}}$${N_{{\rm{FA}}}}$FoM (%)
    SO-CFAR方法[38]12611091.97
    迭代CA-CFAR方法[40]11423083.21
    显著性方法[12]11621084.67
    SPAN+超像素1334097.08
    ${\left| { {\gamma _{ {\rm{HH\text{-}HV} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素1361099.27
    ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{min} } } }$+超像素1352396.43
    ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-} (HV)} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素1361198.55
    多特征融合+超像素1352197.83
    下载: 导出CSV

    表  2  高分三号数据I定量检测结果

    Table  2.   Quantitative detection results of GaoFen-3 data I

    方法${N_{\rm{C}}}$${N_{\rm{M}}}$${N_{{\rm{FA}}}}$FoM (%)
    SO-CFAR方法[38]18359075.62
    迭代CA-CFAR方法[40]16676068.60
    显著性方法[12]21527088.84
    SPAN+超像素17468071.90
    ${\left| { {\gamma _{ {\rm{HH\text{-}HV} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素24021095.24
    ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{min} } } }$+超像素2384895.20
    ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素24111195.26
    多特征融合+超像素2393895.60
    下载: 导出CSV

    表  3  高分三号数据II定量检测结果

    Table  3.   Quantitative detection results of GaoFen-3 data II

    方法${N_{\rm{C}}}$${N_{\rm{M}}}$${N_{{\rm{FA}}}}$FoM (%)
    SO-CFAR方法[38]386184.44
    迭代CA-CFAR方法[40]413682.00
    显著性方法[12]3113070.45
    SPAN+超像素386086.36
    ${\left| { {\gamma _{ {\rm{HH\text{-}HV} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素440295.65
    ${\left| { {\gamma _{ {\rm{(HH-VV)\text{-}(HV)} } } }(\theta )} \right|_{ {\rm{min} } } }$+超像素413093.18
    ${\left| { {\gamma _{ {\rm{(HH-VV) \text{-}(HV)} } } }(\theta )} \right|_{ {\rm{org} } } }$+超像素431391.49
    多特征融合+超像素440197.78
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-19
  • 修回日期:  2021-02-02
  • 网络出版日期:  2021-02-22

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