%0 Journal Article
%T Unsupervised Classification Methods and Experimental Research of Dual-frequency Fully Polarimetric SAR Images<br>双波段全极化SAR图像非监督分类方法及实验研究
%A Liu Xiu-qing
%A Yang Ru-liang
%A Yang Zhen
%A <br>刘秀清
%A 杨汝良
%A 杨震
%J 电子与信息学报
%D 2004
%I 
%X In this paper, initial assumption of SAR pixel distribution is derived from H/a classifier. Then a Maximum Likelihood (ML) method is introduced to improve the classifi-cation.. The backscattering properties of a natural medium, that varies with the observation frequency, dual-frequency SAR images are combined to get further improved classification. Speckle in SAR images will disturb classification accuracy. Vector filter of speckle is used to dual-frequency images before classification. Experiments are done on data got by NASA/JPL lab near Tien Mountains, and pseudo-colored classification results of both single and dual frequency POLSAR image are submitted. Results show that filtered dual-frequency fully polarimetric SAR data obtain the best classification result.
%K Fully polarimetric SAR
%K Unsupervised classification
%K Speckle
%K Vector filtering<br>全极化SAR
%K 非监督分类
%K 相干斑
%K 矢量滤波
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=E628E0BDA45C6ECB&yid=D0E58B75BFD8E51C&vid=96C778EE049EE47D&iid=708DD6B15D2464E8&sid=0F13B34BCC0D8726&eid=55FF287E72D71014&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=8