Unsupervised Classification of PolSAR Images Using Eigenvector Analysis,Krogager Decomposition and the Wishart Classifier

摘要：

In this paper,a new scheme for unsupervised classification of polarimetric synthetic aperture radar (PolSAR) images is presented.The method mainly consists of four parts: eigenvector analysis of the coherency (or covariance)matrix, Krogager decomposition,unsupervised classification using Krogager coefficients and scattering entropy,and iterative classification based on the Wishart distance measure.The method can classify the pixels into nine classes,and its effectiveness is demonstrated using the Jet Propulsion Laboratory s AIRSAR and SIR-C/X-SAR L-band PolSAR data.

作者: Xiao-guang ZHOU Li-wen ZHAO Gang-yao KUANG Jian-wei WAN

作者单位: College of Electronic Science and Engineering National University of Defense Technology Changsha,Hunan,P.R.China,410073

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 161-164

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）