%0 Journal Article
%T Robust Statistical Theory Based RS Image Feature Estimating Model<br>遥感影像特征发现的稳健统计模型研究
%A LUO Jiancheng
%A ZHOU Chenghu
%A Ma Jianghong
%A <br>骆剑承
%A 周成虎
%A 马江洪
%J 中国图象图形学报
%D 1999
%I 
%X Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory. Firstly, GMDD is assumed with a mixture group of Gaussian distribution in feature space, then by optimization algorithm the feature which mostly accord with the assumed distribution is hierarchically extracted from space until all of the features in the space are decomposed to a group of featuring pattern. Compared with conventional statistical clustering methods, GMDD's main outstanding superorities are:(1) Initial number of features does not needed to be specified a priori; (2) The proportion of noisy data in the mixture can be large; (3) The parameters estimation of each feature is virtually initial independent; and (4) The variability in the shape and size of the feature densities in the mixture is taken into account. The article presents the model named the GMDD based remote sensing image feature estimation model (GIFEM) , and the model of GA space searching optimization is also presented out.
%K Robust statistics
%K Gaussian Mixture Density
%K Image features
%K Genetic algorithm<br>稳健统计
%K 影像特征
%K 遗传算法
%K GMDD
%K 遥感影像
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=CB16C971D90C4509&yid=B914830F5B1D1078&vid=E158A972A605785F&iid=708DD6B15D2464E8&sid=793F041A4288469A&eid=F1F574FD407695D4&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=1&reference_num=10