Mixed principal-component-analysis/independent-component-analysis transform for hyperspectral image analysis

Abstract

Principal components analysis (PCA) and independent-component analysis (ICA) are widely used transforms to perform various tasks. Mixing both transforms has not been investigated. This paper develops a new transform, called the mixed PCA/ICA transform, which combines m principal components (PCs) produced by PCA and n independent components (ICs) generated by ICA to form a new set of m+n mixed components to be used for hyperspectral image analysis. Four problems need to be addressed. One is to determine the total number of components, p, needed to be generated for the mixed (m,n)-PCA/ICA transform. The second is how to combine the PCA and ICA in a single transform. Since the ICA does not prioritize its generated ICs in the same way that the PCs are ranked by the PCA using data variances, how to generate an appropriate set of n ICs becomes a third problem. Finally, the fourth problem is to decompress the compressed data based on the mixed PCA/ICA components if there is a need to reconstruct the original data. This paper solves these four problems and further conducts experiments to demonstrate the utility of the mixed PCA/ICA transform in subpixel detection and mixed pixel classification and quantification.