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The framework consisting of a pixel-wise classification followed by a Markov random field has been very successful for spatial-spectral hyperspectral classification. While training such frameworks, the classifier and the Markov random field are generally tuned greedily one after another. However, better results could be obtained by tuning both of the components simultaneously with the objective of...
The hyperspectral unmixing problem can be formulated as a combinatorial optimization which selects the spectral vectors that maximize the volume of a simplex, with the assumptions that the dataset contain pure pixels and the mixture is linear. Submodularity presents an intuitive diminishing returns property which arises naturally in discrete and combinatorial optimization problems. Submodular functions...
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