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.