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Hyperspectral unmixing using double reweighted collaborative sparse regression

An important technique in hyperspectral unmixing is collaborative sparse regression. It improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Now it is well known that introducing weighted factors to enforce sparseness becomes a necessary process in sparse unmixing methods. In this paper, considering the desirable performance of reweighted minimization, a double reweighted collaborative sparse regression (DR-CLSUnSAL) has been utilized. The proposed method enhances the sparsity of abundance factions in both the spectral sparsity (column sparsity of the fractional abundances in the sense) and the spatial sparsity (row sparsity of the fractional abundances in the sense). Then the optimization problem was simply solved by the variable splitting and augmented Lagrangian algorithm. Our experimental results with simulated data sets generated by randomly extracting from the United State Geological Survey(USGS) library demonstrate that the proposed method is an effective and accurate sparse unmixing algorithm compared with other advanced hyperspectral unmixing methods.