Independent Component Analysis as a tool for the dimensionality reduction and the representation of hyperspectral images

Independent Component Analysis (ICA) is a multivariate data analysis process largely sudied these last years in the signal processing community for blind source separation. This paper proposes to show the interest of ICA as a tool for unsupervised analysis of hyperspectral images. The commonly used Principal Component Analysis (PCA) is the mean square optimal projection for gaussian data leading to uncorrelated components by using second order statistics. ICA rather uses higher order statistics and leads to independent components, a stronger statistical assumption revealing interesting features in the usually non gaussian hyperspectral data sets.