Abstract
Morphological Associative Memories (MAM) are a construct similar to Hopfield Associative Memories defined on the (R,+, ∨, ⋀) algebraic system. The MAM posses excellent recall properties for undistorted patterns. However they suffer from the sensitivity to specific noise models, that can be characterized as erosive and dilative noise. We find that this sensitivity may be made of use in the task of Endmember determination for the Spectral Unmixingof Hyperspectral Images.
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Graña, M., Raducanu, B., Sussner, P., Ritter, G. (2002). On Endmember Detection in Hyperspectral Images with Morphological Associative Memories. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_54
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