Abstract
We propose a spectrum selection procedure from hyperspectral images, which uses the Autoassociative Morphological Memories (AMM) as detectors of morphological independence conditions. Selected spectra may be used as endmembers for spectral unmixing. Endmember spectra lie in the vertices of a convex region that covers the image pixel spectra. Therefore, morphological independence is a necessary condition for these vertices. The selective sensitivity of AMM’s to erosive and dilative noise allows their use as morphological independence detectors.
The authors received partial support of the Ministerio de Ciencia y Tecnologia under grant TIC2000-0739-C04-02
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Graña, M., Gallego, J., Torrealdea, F.J., d’Anjou, A. (2003). On the application of Associative Morphological Memories to Hyperspectral Image Analysis. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_72
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DOI: https://doi.org/10.1007/3-540-44869-1_72
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