This paper describes a hybrid technique for reducing the number of potential hyperspectral endmembers determined from the min-WXX and max-MXX lattice auto-associative memories. Given a set X of pixel spectra we first compute vector data sets obtained from shifted versions of WXX and MXX. In a second step, application of hard or fuzzy c-means clustering to the column vectors derived from WXX and MXX lowers the number of candidate endmembers and cluster centroids are approximations to final endmembers. Some validation indexes are computed on generated clusters to provide guidance in finding the number of endmembers required to unmix a hyperspectral image. Examples are given to illustrate how our proposed hybrid technique can be applied in the analysis of hyperspectral imagery.
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