Unmixing Using a Combined Microscopic and Macroscopic Mixture Model with Distinct Endmembers

Abstract:

Much work in the study of hyperspectral imagery has focused on macroscopic mixtures and unmixing via the linear mixing model. A substantially different approach seeks to model hyperspectral data non-linearly in order to accurately describe intimate or microscopic relationships of materials within the image. In this paper we present and discuss a new model (MacMicDEM) that seeks to unify both approaches by representing a pixel as both linearly and non-linearly mixed, with the condition that the endmembers for both mixture types need not be related. Using this model, we develop a method to accurately and quickly unmix data which is both macroscopically and microscopically mixed. Subsequently, this method is then validated on synthetic and real datasets.

 

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Plain Text:

D. Dranishnikov, P. Gader, A. Zare, T. Glenn, “Unmixing using a combined microscopic and macroscopic mixture model with distinct endmembers,” 21st European Signal Processing Conference (EUSPICO 2013) , pp. 1-5, Sept. 9-13, 2013.

 

BibTex:

@INPROCEEDINGS{DranishnikovZare:2013,
author={D. Dranishnikov and P. Gader and A. Zare and T. Glenn},
booktitle={21st European Signal Processing Conference (EUSPICO 2013)}, title={Unmixing using a combined microscopic and macroscopic mixture model with distinct endmembers},
year={2013},
month={Sept.},
pages={1-5},
}

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