A simple associative neural network for producing spatially homogenous spectral abundance interpretations of hyperspectral imagery

A hyperspectral remotely sensed image may be modeled as a linear mixture of the spectral responses of unknown spectral endmembers. Using the a-priori information that the unknown spectral abundance images should be spatially homogenous, a simple associative neural network may be trained using Hebbian learning to extract spectral endmembers and corresponding abundance images from a hyperspectral image. The technique is applied to an AVIRIS image of Cuprite, Nevada and is compared to an interactive technique for approximating the spectral convex hull of a hyperspectral image that requires a-priori geological knowledge to identify spectral endmembers.