Hyperspectral image analysis with associative morphological memories

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. 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. The selective sensitivity of AMM to noise characterized as erosive and dilative noise allows their use as morphological independence detectors.