11 April 2008 A generalized linear mixing model for hyperspectral imagery
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Abstract
We continue previous work that generalizes the traditional linear mixing model from a combination of endmember vectors to a combination of multi-dimensional affine endmember subspaces. This generalization allows the model to handle the natural variation that is present is real-world hyperspectral imagery. Once the endmember subspaces have been defined, the scene may be demixed as usual, allowing for existing post-processing algorithms (classification, etc.) to proceed as-is. In addition, the endmember subspace model naturally incorporates the use of physics-based modeling approaches ('target spaces') in order to identify sub-pixel targets. In this paper, we present a modification to our previous model that uses affine subspaces (as opposed to true linear subspaces) and a new demixing algorithm. We also include experimental results on both synthetic and real-world data, and include a discussion on how well the model fits the real-world data sets.
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David Gillis, Jeffrey Bowles, Emmett J. Ientilucci, David W. Messinger, "A generalized linear mixing model for hyperspectral imagery", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69661B (11 April 2008); doi: 10.1117/12.782113; https://doi.org/10.1117/12.782113
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