Abstract: A variety of hyperspectral image pixel unmixing methods have been developed and are reported in the literature. This paper addresses the use of SOLVER, a constrained optimization technique, implemented as a feature in the Microsoft Excel software package. The method is illustrated on example data from the NEFDS spectral library. Hyperspectral imagery, i.e., imagery with more than a hundred spectral bands, has been shown to be particularly useful for identifying the material constituents of the are imaged. Since each pixel is a spectral signature, comparing that signature with a library of signatures for known materials allows each pixel's material to be identified as the one with the closest match. Since many measures of matching are used in the community, it is attractive that SOLVER allows the specification of any chosen objective function, including nonlinear expressions. This material identification process becomes an unmixing process when the pixel on the ground includes multiple materials; then the pixel is 'mixed' and no one library signature will match. Rather, a sum of library signatures, with appropriate coefficients of proportionality, that matches the pixel's signature must be determined. In this paper hypothetical pixel signatures are constructed from signatures selected from the NEFDS spectral signature library. These hypothetical signatures are then shown to respond well to SOLVER unmixing for diverse cases. !8
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