Recently there has been increased interest in the use of the independent component analysis (ICA) for image analysis. ICA can be considered as one approach to component analysis. Among other approaches, the traditional principal component analysis (PCA) is most popular. The component analysis that extracts the most important components of the data is useful for data mining in remote sensing which normally involves a very large amount of data. While PCA method attempts to decorrelate the components in a vector, ICA methods are to make the components as statistically independent as possible. ICA methods are generally more demanding in computation than PCA. We have developed a joint cumulant ICA (JC-ICA) algorithm which can be implemented efficiently by a neural network. As such it is a very useful tool for data mining in remote sensing. The use of the algorithm especially in hyperspectral image analysis will be presented in this paper.
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