雷达学报2016，Vol.5Issue(2)：217-227,11.DOI:10.12000/JR16019
一种联合特征值信息的全极化SAR图像监督分类方法
Polarimetric SAR Image Supervised Classification Method Integrating Eigenvalues
摘要
Abstract
Since classification methods based onH/a space have the drawback of yielding poor classification results for terrains with similar scattering features, in this study, we propose a polarimetric Synthetic Aperture Radar (SAR) image classification method based on eigenvalues. First, we extract eigenvalues and fit their distribution with an adaptive Gaussian mixture model. Then, using the naive Bayesian classifier, we obtain preliminary classification results. The distribution of eigenvalues in two kinds of terrains may be similar, leading to incorrect classification in the preliminary step. So, we calculate the similarity of every terrain pair, and add them to the similarity table if their similarity is greater than a given threshold. We then apply the Wishart distance-based KNN classifier to these similar pairs to obtain further classification results. We used the proposed method on both airborne and spaceborne SAR datasets, and the results show that our method can overcome the shortcoming of theH/a-based unsupervised classification method for eigenvalues usage, and produces comparable results with the Support Vector Machine (SVM)-based classification method.关键词
极化SAR/地物分类/特征值Key words
Polarimetric Synthetic Aperture Radar (SAR)/Terrain classification/Eigenvalues分类
信息技术与安全科学引用本文复制引用
邢艳肖,张毅,李宁,王宇,胡桂香..一种联合特征值信息的全极化SAR图像监督分类方法[J].雷达学报,2016,5(2):217-227,11.基金项目
国家自然科学基金优秀青年基金(61422113) （61422113）
