Urban-Area Extraction From Polarimetric SAR Images Using Polarization Orientation Angle
Urban-Area Extraction From Polarimetric SAR Images Using Polarization Orientation Angle
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DOI：
10.1109/lgrs.2012.2207085
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发表时间：
2013-03-01
影响因子：
4.8
通讯作者：
Susaki, Junichi
中科院分区：
文献类型：
--
作者：
Kajimoto, Muneyoshi;Susaki, Junichi
本文提出了一种从极化合成孔径雷达图像中稳健提取城市区域的算法。该算法利用了极化方位角(POA)、四分量分解得到的体散射功率(PV)和总功率(TP)。通过由POA旋转相干矩阵的元素，可以减少四个分解分量对POA的依赖。然而，即使在调整之后，对POA的依赖程度仍然存在。该算法利用POA校正后的分量，但根据POA将像素分成几类。首先，为研究区域内的每个类别选择城市和农田训练数据。然后利用PV-TP散射图将城市和山区与农田、裸露的地面和海洋区分开来。最后，使用相邻像素间POA随机性的度量来区分POA接近均匀的城市地区和POA随机分布的山区。当对多个研究区域执行分类时，为其中一个研究区域手动选择的阈值被用于自动估计其他区域的阈值。精度评估表明，基于POA的分类和POA随机性的利用有助于提高分类精度。
In this letter, an algorithm is proposed that robustly extracts urban areas from polarimetric synthetic aperture radar images. Polarization orientation angle (POA), volume scattering power (Pv) derived by four-component decomposition, and total power (TP) are utilized in the proposed algorithm. The dependence of the four decomposition components on POA can be lessened by rotating the elements of the coherency matrix by the POA. However, a level of POA dependence remains even after the correction. The proposed algorithm utilizes POA-corrected components, but pixels are grouped into several categories according to POA. First, urban and farmland training data are selected for each category in a study area. Then, urban and mountain areas are separated from farmland, bare ground, and sea by utilizing the Pv-TP scattergram. Finally, a measure of the POA randomness between neighboring pixels is used to discriminate between urban areas with nearly homogeneous POA and mountain areas with randomly distributed POAs. When performing classification on more than one study area, thresholds manually selected for one of the study areas are used to automatically estimate thresholds for the other areas. An accuracy assessment demonstrates that POA-based categorization and utilization of POA randomness contribute to improving classification accuracy.