Cardinality-constrained sparse spectral unmixing can be solved using Branch-and-Bound algorithms, provided that the number of reference endmembers and the cardinality constraint are reasonably small. However, focusing solely on the best solution may …
We focus on the exact resolution of sparse spectral unmixing problems, that is, the search for cardinality-limited linear least squares solutions under non-negativity and sum-to-one constraints. The originality of the proposed method - for which the Python code is provided - lies in its multisolution nature; we return the set of supports that yield the best solutions. The method is tested on synthetic data, with promising results.
We propose an algorithm that exactly solves the cardinality-constrained sparse spectral unmixing problem.