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.