%0 Journal Article
%T Remote Image Classification Based on Independent Component Analysis<br>基于独立分量分析的遥感图像分类技术
%A ZENG Sheng-gen
%A WANG Xiao-nin
%A FAN Rui-bin
%A XIA De-shen
%A <br>曾生根
%A 王小敏
%A 范瑞彬
%A 夏德深
%J 遥感学报
%D 2004
%I 
%X The automatic classification methods for remote sensing images are usually based on statistic information of the images. It has correlation among multi-spectral remote sensing images, and the correlation is a disadvantage to automatic classification of remote images. Commonly, Principal Component Analysis (PCA) is used to remove the correlation. Independent Component Analysis (ICA) can obtain higher order statistics information than PCA. It not only can remove the correlation, and also can obtain band images that are mutual independent. Firstly the fundamental of Independent Component Analysis is briefly introduced. Then, a fast algorithm of ICA (FastICA) and its modification (M-FastICA) are introduced, and are used to classify the remote sensing images. In the result, compare to basic FastICA algorithm, M-FastICA runs quickly and has better convergence performance, and improves the validity of the ICA in classifying of the remote sensing images.
%K independent component analysis
%K principal component analysis
%K fixed-point
%K remote image
%K self-adaptive min-distance classification<br>独立分量分析
%K 主成分分析
%K 固定点算法
%K 遥感图像
%K 自适应最小距离分类法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=E7E786015A322ED3&yid=D0E58B75BFD8E51C&vid=5D311CA918CA9A03&iid=0B39A22176CE99FB&sid=2DBBF45CC176713E&eid=0B4F496D54044D86&journal_id=1007-4619&journal_name=遥感学报&referenced_num=19&reference_num=20