Sparse Unmixing of Hyperspectral Data: The Legacy of SUnSAL

In the last decade, the sparse regression approach was established as a new paradigm in hyperspectral unmixing. This paper reviews various directions in sparse unmixing, starting from the initial formulation proposed by Prof. Jose Bioucas-Dias: Sparse Unmixing via variable Splitting and Augmented Lagrangian (SUnSAL). SUnSAL has paved the path towards algorithms accounting for spatial homogeneity, data collaborativity, structured dictionaries, among others. Despite being the first sparse regression algorithm widely exploited in hyperspectral unmixing, SUnSAL can be still considered competitive and its legacy lies in the plethora of subsequent algorithms that it inspired.