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POA Correction Method Using High-resolution Full-polarization SAR Image
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摘要: 城市地区极化分解对于监测城市扩张速度并研究其对生态环境的影响非常重要。全极化合成孔径雷达(PolSAR)允许我们使用极化目标分解方法来检测地物散射机制，是一种对城区变化进行监测的手段。然而，目标的极化方向角(POA)会影响极化分解结果，导致体散射过估等问题，使得极化分解结果不能正确地体现目标的散射机理。传统的去取向方法只对主导极化方向角较小的地区起到去取向的作用，对取向角较大区域没有明显的效果。该文针对以上问题提出了一种基于高分辨率城市区域图像的POA校正方法。首先，高分辨率图像中城市建筑区域的POA会因为地物变化出现跳变的现象，可以利用POA的随机性对城市地区的范围进行估计。其次，使用线性逼近方法来获取城区中使交叉极化项最小的POA。利用该文提出的POA校正算法，可以使取向角导致的误分解问题得到缓解，提高分解结果的准确度。该算法使用2009年于四川都江堰地区获取的机载X波段全极化数据进行验证，得到了明显的改进结果，城市区域的体散射分量得到了显著的提升。Abstract: Polarimetric decomposition in urban areas is important for monitoring the speed of city expansion and studying its ecological environmental influence. Using fully Polarimetric Synthetic Aperture Radar (PolSAR) is a method for consistent observation of large-range urban changes. In the last two decades, most research on decomposition methods have stated that Polarization Orientation Angle (POA) would affect the results of decomposition by overestimating the volume scattering contribution of urban areas. The available deorientation methods cannot rotate built-up areas with large POAs. This paper proposes an algorithm for decomposition of high-resolution urban area images based on a POA correction method. First, for high-resolution images of built-up areas, the POA changes radically pixel by pixel. An approximate assessment of urban areas can be accomplished using POA randomness. Then, to search for the true POA of large dominant POA areas (most built-up regions), the linear approximation method is used to locate POAs that can minimize cross-polarized terms. Thereby, the inaccurate decomposition that occurs by the deviation of POA can be fixed, and the accuracy of results improves. The fully PolSAR data of the Dujiangyan area in Sichuan Province, China are used to confirm the algorithm’s effectiveness. The data are acquired by an X-band airborne SAR sensor designed by the Institute of Electronics, China Academy of Sciences (IECAS).
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图 4 算法流程第1部分OP计算工具图
Figure 4. The images used to count OP in the first part of the flowchart
图 8 A, B, C区域异构参数分布图
Figure 8. Distribution diagram of heterogeneous parameter in district A, B and C
图 9 异构参数阈值为7到12时都江堰地区图像状态
Figure 9. Approximate results of distinguished built-up areas with different threshold values of the heterogeneous parameter. (a)–(f) Show the results with the thresholds of 7 to 12, respectively
图 10 传统方法和本文方法计算得到的都江堰地区的取向角
Figure 10. POA of the Dujiangyan area calculated by the traditional method and the new method
图 12 A, B, C地区使用传统算法和本文算法后得到的T33值
Figure 12. T33 of districts A, B and C after rotation using POAs calculated by the traditional method and the new method
图 13 A, B, C地区使用新算法进行POA校正后分解结果与Pauli分解算法对比图
Figure 13. Pauli decomposition results and results of decomposition using POA correction method proposed in this paper of districts A, B and C, respectively
图 11 使用基于高分辨率图像的POA校正算法进行去取向后的分解结果
Figure 11. Result of Dujiangyan image decomposition using POA correction method proposed in this paper
表 1 A, B, C区域中交叉极化T33项平均值
Table 1. Average T33 of districts A, B and C after rotation using POAs calculated by the traditional method and the new method
区域 传统方法T33平均值 本文方法T33平均值 A 4.6321e–08 1.5611e–08 B 3.5252e–08 3.5252e–08 C 2.9456e–08 1.3435e–08 
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表 2 A, B, C区域中各散射分量占总功率的百分比
Table 2. Percentage of scattering powers with traditional method and the new method
区域 算法 Ps Pv Pd A 传统算法 1.04 84.83 2.69 本文算法 20.29 62.84 2.84 B 传统算法 0.74 90.35 2.79 本文算法 14.72 72.61 10.16 C 传统算法 29.34 23.60 43.85 本文算法 18.35 20.73 56.06
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