Research Papers

Hyperspectral unmixing using macroscopic and microscopic mixture models

[+] Author Affiliations
Ryan Close

US Army RDECOM CERDEC NVESD, 10221 Burbeck Road, Fort Belvoir, Virginia 22060

Paul Gader, Joseph Wilson

University of Florida, E301 CSE Building, P.O. Box 116120, Gainesville, Florida 32611

J. Appl. Remote Sens. 8(1), 083642 (Apr 29, 2014). doi:10.1117/1.JRS.8.083642
History: Received September 18, 2013; Revised March 12, 2014; Accepted April 1, 2014
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Abstract.  Macroscopic and microscopic mixture models and algorithms for hyperspectral unmixing are presented. Unmixing algorithms are derived from an objective function. The objective function incorporates the linear mixture model for macroscopic unmixing and a nonlinear mixture model for microscopic unmixing. The nonlinear mixture model is derived from a bidirectional reflectance distribution function for microscopic mixtures. The algorithm is designed to unmix hyperspectral images composed of macroscopic or microscopic mixtures. The mixture types and abundances at each pixel can be estimated directly from the data without prior knowledge of mixture types. Endmembers can also be estimated. Results are presented using synthetic data sets of macroscopic and microscopic mixtures and using well-known, well-characterized laboratory data sets. The unmixing accuracy of this new physics-based algorithm is compared to linear methods and to results published for other nonlinear models. The proposed method achieves the best unmixing accuracy.

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Citation

Ryan Close ; Paul Gader and Joseph Wilson
"Hyperspectral unmixing using macroscopic and microscopic mixture models", J. Appl. Remote Sens. 8(1), 083642 (Apr 29, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083642


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