结合特征选择和大尺度谱聚类的极化SAR图像非监督分类

非监督分类是极化SAR图像解译的重要手段，但其分类结果易受到高维特征的影响。针对此问题，本文提出一种结合特征选择和大尺度谱聚类的极化SAR图像非监督分类方法。该方法首先深入分析并提取了极化SAR图像分类中常用的特征参数，包括基于测量数据及其简单线性变换的特征和极化目标分解的特征。然后通过聚类森林特征选择算法进行特征降维处理，去除冗余信息。最后利用过分割产生代表点并构建原始数据与代表点间的二分图，通过大尺度谱聚类算法完成图像的非监督分类。实验结果表明，该方法能够选取有效的特征组合，并得到较为满意的分类效果。 

Unsupervised Classification of PolSAR Images by Combining Feature Selection and Large Scale Spectral Clustering

Unsupervised classification is an important tool for PolSAR image interpretation,which is easily affected by the high dimensional features.Thus, we propose an unsupervised classification algorithm for PolSAR images by combining feature selection and large scale spectral clustering.First, the widely used features are extracted from PolSAR images including the features based on the simple linear transformation of raw data and polarimetric target decomposition. Secondly, in order to remove redundant information and facilitate later analysis, a cluster forest based feature selection algorithm is used to perform dimension reduction. Finally, the representative points are generated by an over segmentation method, and the bipartite graph is constructed between raw data points and these representative points.The unsupervised classification result is obtained by using a large scale spectral clustering method which employs a representative point based scheme.The experiments show that the proposed method can select an effective feature set and obtain a satisfactory result for unsupervised classification of PolSAR images.