Abstract:

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.

Links:

Citation:

R. Close, P. Gader, A. Zare, J. Wilson, and D. Dranishnikov, “Endmember extraction using the physics-based multi-mixture pixel model,” in Proc. SPIE 8515, Imaging Spectrometry XVII, 2012. 

@InProceedings{close2012endmember,
Title = {Endmember extraction using the physics-based multi-mixture pixel model},
Author = {Ryan Close and Paul Gader and Alina Zare and Joseph Wilson and Dmitri Dranishnikov},
Booktitle = {Proc. SPIE 8515, Imaging Spectrometry XVII},
Year = {2012},
Month = {Oct.},
Number = {85150L},
Volume = {8515},
Doi = {10.1117/12.930288},
}