Endmember extraction using the physics-based multi-mixture pixel model


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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Plain Text:

R. Close, P. Gader, A. Zare, J. Wilson, D. Dranishnikov, “Endmember extraction using the physics-based multi-mixture pixel model,” Proceedings of SPIE Vol. 8515, 85150L (2012).


author={R. Close and P. Gader and A. Zare and J. Wilson and D. Dranishnikov},
journal={Proceedings of SPIE},
title={Endmember extraction using the physics-based multi-mixture pixel model},

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