Linear unmixing-based feature extraction for hyperspectral data in a high performance computing environment

Stefan A. Robila

doi:10.1117/12.928666

15 October 2012 Linear unmixing-based feature extraction for hyperspectral data in a high performance computing environment

Stefan A. Robila

Author Affiliations +

Proceedings Volume 8515, Imaging Spectrometry XVII; 85150M (2012) https://doi.org/10.1117/12.928666
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

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.

Citation Download Citation

Stefan A. Robila "Linear unmixing-based feature extraction for hyperspectral data in a high performance computing environment", Proc. SPIE 8515, Imaging Spectrometry XVII, 85150M (15 October 2012); https://doi.org/10.1117/12.928666

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