Hierarchical alternating least squares algorithm with Sparsity Constraint for hyperspectral unmixing

In this paper, we not only extend the temporal hierarchical alternating least squares (HALS) to spatial domain, but also incorporate two necessary characteristics of material abundances, full additivity and sparsity, to unmix hyperspectral data. The new algorithm is abbreviated as HALSSC (HALS with Sparsity Constraint). Different from the other endmember extraction approaches, the proposed algorithm does not need the existence assumption of pure pixel of each endmember in the scene. Experimental results on highly mixed synthetic data and real hyperspectral data from Washington DC mall confirm the accuracy of the developed algorithm.