Spatial weighted sparse regression for hyperspectral image unmixing

Abstract

Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on analyzing the hyperspectral data without fully incorporating the spatial information. To address this limitation, a spatial weighted sparse unmixing (SWSU) algorithm is proposed in this paper, which can take full advantage of the spatial information and further enhance the sparsity of the abundances. This is done by incorporating local neighborhood weights into the double reweighted sparse unmixing formulation. Experimental results on simulated hyperspectral data sets illustrate the good potential of the spatial weighted strategy for sparse unmixing introduced in this paper, which can greatly improve abundance estimation results.