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Refined Ship Feature Characterization Method of Full-polarimetric Synthetic Aperture Radar for Visual Interpretation
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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。Abstract: With advances in satellite technology, Polarimetric Synthetic Aperture Radar (PolSAR) now have higher resolution and better data quality, providing excellent data conditions for the refined visual interpretation of artificial targets. The primary method currently used is a multicomponent decomposition, but this method can result in pixel misdivision problems. Thus, we propose a non-fixed threshold division method for achieving advanced feature ship structure characterization in full-polarimetric SAR images. Yamaguchi decomposition can effectively identify the primary scattering mechanism and characterize artificial targets. Its modified volume scattering model is more consistent with actual data. The polarization entropy can serve as the target scattering mechanism at a specified equivalent point in the weakly depolarized state, which can effectively highlight the ship structure. This paper combines the three components of the Yamaguchi decomposition algorithm with the entropy, and divides it into a nine-classification plane with a non-fixed threshold. This method reduces category randomness generated by noise at the threshold boundary for complicated threshold treatments. Furthermore, the Mixed Scattering Mechanism (MSM) which is the region where both secondary scattering and single scattering are significant, was proposed to better match the scattering types of typical structures of vessels in the experiment. The Generalized Similarity Parameter (GSP) was used to further shorten the intra-class distance and perform iterative clustering using a modified GSP-Wishart classifier. This method improves the vessel distinguishability by enhancing the secondary and mixed scattering mechanisms. Finally, this paper uses full-polarimetric SAR data from a port in Shanghai, China, for the experiment. We collected and filtered ship information and optical data from this port through the Automatic Identification System (AIS) and matched them with the ships in full-polarimetric SAR images to verify the correct characterization of each vessel’s features. The experimental results show that the proposed method can effectively distinguish three types of vessels: bulk carriers, container ships and tankers.
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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 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 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 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 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 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图 理想状态下船只散射机制图 散货船 
集装箱船 
油轮 
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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 ${ {\boldsymbol{T} }_{{\rm{V}}} }$ ${R}{ < - 2 \;{\rm{dB} } }$ $\dfrac{ {1} }{ { {30} } }\left[ {\begin{array}{*{20}{c} } {15}&{5}&{0} \\ {5}&{7}&{0} \\ {0}&{0}&{8} \end{array} } \right]$ ${ - 2 \;{\rm{dB} } < }{R}{ < 2 \;{\rm{dB} } }$ $\dfrac{ {1} }{ {4} }\left[ {\begin{array}{*{20}{l} } {2}& {0}& {0} \\ {0}& {1}& {0} \\ {0}& {0}& {1} \end{array} } \right]$ ${R}{ > 2 \;{\rm{dB} } }$ $\dfrac{ {1} }{ { {30} } }\left[ {\begin{array}{*{20}{c} } { {15} }& { { - 5} }& {0} \\ { { - 5} }& {7}& {0} \\ {0}& {0}& {8} \end{array} } \right]$
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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T和PS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
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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
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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 414330000 SHEN YU 69 散货船 191 32 
413240950 GAO LAN 309 散货船 155 23 
413653000 QIU JIN 集装箱船 135 21 
413697150 XIN BIN HE 集装箱船 156 21 
413335470 HANG HAI JIA 369 油轮 118 17 
413269010 NING SHEN HAI HUA 11 油轮 133 21 
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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 $-GSPWishart Yamaguchi/H-Wishart Yamaguchi/H-GSPWishart Ld M Hd S V Ld M Hd S V Ld M Hd S V Ld M Hd S V 散货船 1 9 0 13 35 43 9 0 28 31 32 6 6 15 12 61 17 18 6 47 12 2 16 0 20 35 29 18 0 18 34 30 6 24 19 16 35 11 28 21 24 16 集装箱船 3 14 0 33 0 53 15 0 36 0 49 13 28 0 0 59 19 38 0 0 43 4 20 0 17 1 62 25 0 17 2 56 14 21 0 0 65 25 30 0 0 45 油轮 5 6 0 11 0 83 13 0 15 1 71 5 3 37 13 42 4 6 19 4 67 6 5 39 22 0 45 7 38 0 7 48 23 54 0 0 23 16 53 0 0 31 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。
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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
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