面向目视解译的全极化SAR船只精细化特征表征方法

邓莎萨 张帆 尹嫱 马飞 袁新哲

邓莎萨, 张帆, 尹嫱, 等. 面向目视解译的全极化SAR船只精细化特征表征方法[J]. 雷达学报(中英文), 2024, 13(2): 374–395. doi: 10.12000/JR23078
引用本文: 邓莎萨, 张帆, 尹嫱, 等. 面向目视解译的全极化SAR船只精细化特征表征方法[J]. 雷达学报(中英文), 2024, 13(2): 374–395. doi: 10.12000/JR23078
DENG Shasa, ZHANG Fan, YIN Qiang, et al. Refined ship feature characterization method of full-polarimetric synthetic aperture radar for visual interpretation[J]. Journal of Radars, 2024, 13(2): 374–395. doi: 10.12000/JR23078
Citation: DENG Shasa, ZHANG Fan, YIN Qiang, et al. Refined ship feature characterization method of full-polarimetric synthetic aperture radar for visual interpretation[J]. Journal of Radars, 2024, 13(2): 374–395. doi: 10.12000/JR23078

面向目视解译的全极化SAR船只精细化特征表征方法

doi: 10.12000/JR23078
基金项目: 国家自然科学基金(62201027, 62271034)
详细信息
    作者简介:

    邓莎萨,硕士生,主要研究方向为极化SAR图像解译

    张 帆,博士,教授,主要研究方向为SAR信号处理、SAR图像解译、高性能计算等

    尹 嫱,博士,副教授,主要研究方向为雷达遥感图像处理与应用、极化特征的机器学习分类、散射建模与定量化反演

    马 飞,博士,副教授,主要研究方向为SAR图像处理、机器学习、人工智能和目标检测

    袁新哲,博士,副研究员,主要研究方向为SAR海洋遥感

    通讯作者:

    尹嫱 yinq@buct.edu.cn

  • 责任主编:刘涛 Corresponding Editor: LIU Tao
  • 中图分类号: TP75

Refined Ship Feature Characterization Method of Full-polarimetric Synthetic Aperture Radar for Visual Interpretation

Funds: The National Natural Science Foundation of China (62201027, 62271034)
More Information
  • 摘要: 随着卫星技术的发展,极化合成孔径雷达(PolSAR)数据的分辨率和数据质量得到大幅提升,为人造目标的精细化目视解译提供了良好的数据条件。目前主要采用多分量分解的方法,但是易造成像素错分问题,为此,该文结合Yamaguchi极化分解和极化熵提出了一种非固定阈值划分的方法用于实现全极化SAR图像船只结构精细化特征表征。Yamaguchi极化分解能够识别基本散射机制,其修正后的体散射模型更符合实测数据,可有效对人造目标进行表征。极化熵H在弱去极化状态下可以看成某一指定等效点的目标散射机制,能够有效突出船只主散射特征。因此,该文通过将Yamaguchi极化分解算法的非固定三分量与极化熵的低中高熵内嵌,将其分为非固定阈值的九分类成分,从而降低硬阈值处理在阈值边界处受噪声影响产生的类别随机性。并且将二次散射和单次散射均显著的区域称为混合散射(MSM),以更好匹配实验中船只典型结构的散射类型。在此基础上,利用广义相似性参数进一步缩短类内距离,采用改进后的GSP-Wishart分类器进行迭代聚类,旨在通过提高二次散射和混合散射机制以提高不同类型船只可区分度。最后,该文采用中国上海某港口的高分三号全极化SAR数据进行实验,为了验证每艘船只特征表征正确性,通过船舶自动识别系统(AIS)收集并筛选了该港口船只信息及光学数据,并与极化SAR数据中每艘船只进行匹配。实验结果表明该方法可有效区分散货船、集装箱船和油轮3种类型船只。

     

  • 图  1  散货船、集装箱船和油轮的光学图像

    Figure  1.  Optical images of bulk carriers, container ships and oil tankers

    图  2  船只散射机制示意图

    Figure  2.  Scattering mechanism of the vessels

    图  3  本文方法流程图

    Figure  3.  Flow chart of the proposed method

    图  4  非固定阈值划分方法流程图

    Figure  4.  Flow chart of the non-fixed threshold division method

    图  5  非固定阈值划分方法初分类平面图

    Figure  5.  Initial classification plot of the non-fixed threshold division method

    图  6  上海地区全极化Pauli伪彩色图

    Figure  6.  Pauli pseudo-color image of the FP-SAR over Shanghai, China

    图  7  散货船实验结果图

    Figure  7.  Results of the proposed method for bulk carriers

    图  8  集装箱船实验结果图

    Figure  8.  Results of the proposed method for container ships

    图  9  油轮实验结果图

    Figure  9.  Results of the proposed method for oil tankers

    图  10  散货船4种方法结果对比图

    Figure  10.  Results of the four images for bulk carriers

    图  11  散货船4种方法对应的散射机制占比图

    Figure  11.  The scattering feature ratio corresponding to these four methods for bulk carriers

    图  12  集装箱船4种方法结果对比图

    Figure  12.  Results of the four images for container ships

    图  13  集装箱船4种方法对应的散射机制占比图

    Figure  13.  The scattering feature ratio corresponding to these four methods for container ships

    图  14  油轮4种方法结果对比图

    Figure  14.  Results of the four images for oil tankers

    图  15  油轮4种方法对应的散射机制占比图

    Figure  15.  The scattering feature ratio corresponding to these four methods for oil tankers

    图  16  Yamaguchi/H有效性验证

    Figure  16.  Yamaguchi/H validation of validity

    图  17  GSP-Wishart有效性验证

    Figure  17.  GSP-Wishart validation of validity

    图  18  散货船多分量分解方法与本文方法对比图

    Figure  18.  Comparison results between the multi-component decomposition methods and the proposed method of bulk carriers

    图  19  集装箱船多分量分解方法与本文方法对比图

    Figure  19.  Comparison results between the multi-component decomposition methods and the proposed method of container ships

    图  20  油轮多分量分解方法与本文方法对比图

    Figure  20.  Comparison results between the multi-component decomposition methods and the proposed method of oil tankers

    图  21  本文方法下3种类型船只各类散射机制的平均像素数

    Figure  21.  The average number of pixels for each type of scattering mechanism for the three types of ships as processed method

    表  1  3种类型船只的Yamaguchi分解图及对应理想状态下散射机制图

    Table  1.   Yamaguchi decomposition images and the corresponding ideal images of three types of ships

    类型RGB图CIE图PD理想状态下船只散射机制图
    散货船
    集装箱船
    油轮
    下载: 导出CSV

    表  2  不同场景下对应的体散射矩阵

    Table  2.   Volume scattering matrix corresponding to different conditions

    R${ {\boldsymbol{T} }_{{\rm{V}}} }$
    ${R}{ < - 2 \;{\rm{dB} } }$$\dfrac{ {1} }{ { {30} } }\left[ {\begin{array}{*{20}{c} } {15}&{5}&{0} \\ {5}&{7}&{0} \\ {0}&{0}&{8} \end{array} } \right]$
    ${ - 2 \;{\rm{dB} } < }{R}{ < 2 \;{\rm{dB} } }$$\dfrac{ {1} }{ {4} }\left[ {\begin{array}{*{20}{l} } {2}& {0}& {0} \\ {0}& {1}& {0} \\ {0}& {0}& {1} \end{array} } \right]$
    ${R}{ > 2 \;{\rm{dB} } }$$\dfrac{ {1} }{ { {30} } }\left[ {\begin{array}{*{20}{c} } { {15} }& { { - 5} }& {0} \\ { { - 5} }& {7}& {0} \\ {0}& {0}& {8} \end{array} } \right]$
    下载: 导出CSV

    1  Yamaguchi/H-GSPWishart算法流程

    1.   Yamaguchi/H-GSPWishart algorithm

     1. 初始化:设置参数设置聚类最大迭代次数maxiter;
     2. begin:
     3. for 对每一像素Ni
     4.    计算特征值和特征向量,通过式(3)计算极化熵H
     5.    利用式(4)计算整体功率TP,式(2)计算同极化比R,选
         择对应体散射矩阵TV
     6.    分别计算体散射功率PV和螺旋散射功率PC
     7.    if PV+PC<TP
     8.      通过相干矩阵TPS计算T0
     9.      if Re(C0)<0
     10.       通过极化熵H内嵌计算二次散射三分类;
     11.     else if Re(C0)$\ge$0
     12.       通过极化熵H内嵌可得单次散射三分类;
     13.     end
     14.    else
     15.   通过极化熵H内嵌可得体散射三分类;
     16. end
     17. while iter≤maxiter
     18.    for 对每一像素Ni
     19.      计算初分类每一类别的平均相干矩阵T得到聚类
            中心;
     20.      通过式(6)分别计算每一像素旋转后的相干矩阵
            A及聚类中心矩阵B
     21.      通过式(7)分别计算每一像素点与类中心的广义相
            似性参数;
     22.      通过式(9)计算每一像素元到聚类中心的GSP-
            Wishart距离,找到距离最小的所属类别;
     23.    end
     24.    iter = iter+1
     25.    更新聚类中心;
     26. end
     27. 输出结果
    下载: 导出CSV

    表  3  上海地区全极化数据参数

    Table  3.   GaoFen-3 satellite parameters of the FP-SAR over Shanghai, China

    参数指标
    极化方式全极化
    成像模式全极化条带1 (QPSI)
    产品形式单视复数产品(SLC)
    升/降轨升轨ASC
    标准分辨率8 m
    长度×宽度8074 m×7882 m
    长度分辨率4.762506 m
    宽度分辨率2.248443 m
    视数1×1
    下载: 导出CSV

    表  4  上海地区典型船只信息和光学图像

    Table  4.   Information and optical images of typical ships over Shanghai, China

    MMSI船只名称船只类型长度(m)宽度(m)光学图像
    414330000SHEN YU 69散货船19132
    413240950GAO LAN 309散货船15523
    413653000QIU JIN集装箱船13521
    413697150XIN BIN HE集装箱船15621
    413335470HANG HAI JIA 369油轮11817
    413269010NING SHEN HAI HUA 11油轮13321
    下载: 导出CSV

    表  5  4种方法下船只散射分量占比表

    Table  5.   The scattering feature ratio corresponding to these four methods

    船只类型$ H/\bar \alpha $-Wishart$ H/\bar \alpha $-GSPWishartYamaguchi/H-WishartYamaguchi/H-GSPWishart
    LdMHdSVLdMHdSVLdMHdSVLdMHdSV
    散货船190133543 9028313266151261171864712
    21602035291801834306241916351128212416
    集装箱船314033053150360491328005919380043
    420017162250172561421006525300045
    油轮5601108313015171533713424619467
    65392204573807482354002316530031
    注:加粗的数值表示在4种方法中低熵二次散射和混合散射所占比最大的方法。
    下载: 导出CSV

    表  6  3种类型船只散射机理

    Table  6.   Refinement scattering mechanism of three types of ships

    类型理想散射机制图本文精细化结果图散射机理
    散货船船只中段以中轴线上排布的离散红色
    二次散射为主,呈明显点状排布,
    上层甲板以单次散射和混合散射为主
    集装箱船船只中段呈现与船只宽度相当的密集
    红色二次散射,上层甲板以单次散射和
    二次散射叠加而成,呈现为紫色混合散射
    油轮船首船尾以红色二次散射为主,船只中段以绿色体散射为主
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-09
  • 修回日期:  2023-06-11
  • 网络出版日期:  2023-07-10
  • 刊出日期:  2024-04-28

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