Remote Image Classification Based on Independent Component Analysis
基于独立分量分析的遥感图像分类技术

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.