A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm

In hyperspectral sparse unmixing, a successful approach uses spectral bundles to address the variability of the endmembers (EMs) in the spatial domain. However, the regularization penalties usually used aggregate substantial computational complexity, and the solutions are very noise-sensitive. We generalize a multiscale spatial regularization approach to solve the unmixing problem by incorporating group sparsity-inducing mixed norms. Then, we propose a noise-robust method that can take advantage of the bundle structure to deal with EM variability while ensuring inter- and intraclass sparsity in abundance estimation with reasonable computational cost. We also present a general heuristic to select the most representative abundance estimation over multiple runs of the unmixing process, yielding a solution that is robust and highly reproducible. Experiments illustrate the robustness and consistency of the results when compared with related methods.