Using source separation methods for endmember selection

We develop a method for automatic end-member selection in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of an unknown number of materials. In contrast to many end-member selection methods, the new method selects end-members based on the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We show properties of the method by application to synthetic hyperspectral data.