A method of incorporating the multi-mixture pixel model into hyperspectral endmember extraction is presented and
discussed. A vast majority of hyperspectral endmember extraction methods rely on the linear mixture model to describe
pixel spectra resulting from mixtures of endmembers. Methods exist to unmix hyperspectral pixels using nonlinear
models, but rely on severely limiting assumptions or estimations of the nonlinearity. This paper will present a
hyperspectral pixel endmember extraction method that utilizes the bidirectional reflectance distribution function to
model microscopic mixtures. Using this model, along with the linear mixture model to incorporate macroscopic
mixtures, this method is able to accurately unmix hyperspectral images composed of both macroscopic and microscopic
mixtures. The mixtures are estimated directly from the hyperspectral data without the need for a priori knowledge of the
mixture types. Results are presented using synthetic datasets, of multi-mixture pixels, to demonstrate the increased
accuracy in unmixing using this new physics-based method over linear methods. In addition, results are presented using
a well-known laboratory dataset.
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