（英）集成颜色特征和统计特征的极化SAR图像城区分类框架

在传统的利用极化合成孔径雷达(PolSAR)遥感图像分类中，除了近期一个有监督分类的工作，很少涉及颜色特征。与该工作不同，在本文中，针对城区分类，利用颜色特征构造一个新颖的无监督的分类框架。首先，基于最近提出的PolSAR数据的四分量分解模型，计算了常用的颜色空间：YUV，RGB，HSI和CIELab，通过引入颜色熵量化的选择颜色特征，然后，联合纹理特征和扩展的散射功率熵，用自适应的均值漂移算法分割PolSAR图像，最后，根据基于G0分布的距离测度合并聚簇为较为匀质的地物类别。通过L波段AIRSAR数据和C波段Radarsat-2的PolSAR数据验证了提出算法的有效性，分类正确率表明，相比于已有的工作，提出的算法对于城区有较好的区分能力。

A Framework for Classification of Urban Areas Using Polarimetric SAR Images Integrating Color Features and Statistical Model

In conventional terrain classification for the polarimetric SAR (PolSAR) images, color features are rarely involved unless in one recent supervised work. Unlike that work, the color features are exploited in a novel framework for the unsupervised classification of urban areas in this paper. Firstly, based on the recent four-component decomposition model of the PolSAR data, the common color spaces, such as YUV, RGB, HSI, and CIELab are calculated. The color feature is quantitatively selected from these color spaces by introducing the color entropy. Then together with the texture feature and the extended scattering power entropy, the adaptive mean-shift algorithm is used to segment the PolSAR data into clusters. Finally, the clusters are merged according to the G0 distribution-based distance measurement. The proposed framework is verified by the experiments on one AIRSAR L-band and two Radarsat-2 C-band PolSAR data. The classification accuracy indicates that the proposed method has superior discriminative ability for urban areas compared with existing works.