| 基于极化特征和纹理特征的PolSAR影像建筑物提取方法 |
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| 作者姓名： | 马肖肖 程博 刘岳明 崔师爱 梁琛彬 |
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| 作者单位： | 1. 中国科学院遥感与数字地球研究所, 北京 100094;
2. 中国科学院大学, 北京 100049;
3. 中国科学院地理科学与资源研究所, 北京 100101 |
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| 基金项目： | 国家自然科学基金（61372189）资助 |
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| 摘 要： | 极化合成孔径雷达（PolSAR）以其多参数、多通道、多极化、信息记录更加完整等特点，在城市地物提取领域中发挥着重要作用，并已成为遥感影像研究领域的热点。选择覆盖苏州市的Radarsat2影像，利用极化非相干分解法和灰度共生矩阵法分别提取19种极化特征和8种纹理特征，通过分析建筑物、植被和水体的极化特征和纹理特征进行特征组合，结合主成分分析法（PCA）和支持向量机法（SVM）对城市建筑物进行提取，并定量评估精度。结果表明：基于极化特征的建筑物提取精度最高为92.4%；基于纹理特征的提取精度最高为88.9%；极化特征与纹理特征相结合可以提高精度，最高精度为93.7%；PCA特征融合算法具有较高的运算效率，同时提高了精度。
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| 关 键 词： | 极化分解 极化特征 PolSAR 建筑物提取 PCA特征融合 |
| 收稿时间： | 2018-03-19 |
| 修稿时间： | 2018-05-08 |
PolSAR remote sensing image method for building extraction based on polarization and texture characteristics |
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| Authors: | MA Xiaoxiao CHENG Bo LIU Yueming CUI Shiai LIANG Chenbin |
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| Institution: | 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | Polarimetric synthetic aperture radar (PolSAR) plays an important role in the field of building extraction because of its multi-parameter, multi-channel, multi-polarization, and rich information records. Taking Radarsat-2 image of Suzhou in 2017 as an example,19 polarization features and 8 texture features are extracted by polarization non-coherent decomposition methods and GLCM, respectively. Based on the analysis of features, we obtain the results of building extraction by PCA feature fusion and SVM algorithm. The results show that the extraction accuracies based on polarization features and texture feature are 92.4% and 88.9%, respectively. The accuracy is 93.7% when the polarization and texture features are used together. The combination of polarization and texture features improves the accuracy and the PCA feature fusion increases both efficiency and precision. |
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| Keywords: | polarization decomposition polarization characteristics PolSAR building extraction PCA feature fusion |
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