Associative Morphological Memories for Spectral Unmixing

摘要

Unlimited storage and perfect recall of noiseless real valued patterns has been proved for Autoassociative Morphological Memories (AMM). However AMM's suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, Spectral Unmixing of Hyperespectral Images needs the prior definition of a set of Endmembers, which correspond to material spectra lying on vertices of a convex region covering the image data. These vertices can be characterized as morphologically independent patterns. We present a procedure that takes advantade of the AMM's noise sensitivity to perform Endmember spectra selection based on this characterization.