11 April 2008 A generalized linear mixing model for hyperspectral imagery
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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, David Gillis, Jeffrey Bowles, Jeffrey Bowles, Emmett J. Ientilucci, Emmett J. Ientilucci, David W. Messinger, 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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