Associative morphological memories for endmember induction

Spectral unmixing of hyperspectral images relies on the knowledge of a set of endmembers, which are usually unknown. One approach is the induction from the image data of the endmember spectra. Endmember spectra correspond to vertices of a convex region that covers the image pixel spectra. The morphological independence, after shifting the data to zero mean, is a necessary condition for these vertices. We propose a procedure for extraction of spectra from hyperspectral images that may be used as endmembers for unmixing which uses the Autoassociative Morphological Memories (AMM) as detectors of morphological independence conditions.