A novel graph-based approach for segmenting hyperspectral images which guarantees similarity of pixels (spectra) inside each segment.
In this work, we present theoretical guarantees for the performance of a sparse regression based unmixing implemented in the form of a Lasso optimization with non-negativity constraints.
We consider the application of non-homogeneous hidden Markov chain (NHMC) models to the problem of hyperspectral signature classification. It has been previously shown that the NHMC model enables the detection of several semantic structural features of hyperspectral signatures.
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