Random-projection-based nonnegative least squares for hyperspectral image unmixing

Nonnegative least squares, a state-of-the-art approach to endmember abundance estimation in the hyperspectral-unmixing problem, is coupled with random projection employed for dimensionality reduction. Both Hadamard- and Gaussian-based projections are considered. Experimental results reveal that random projections can significantly reduce data volume without detrimentally affecting the accuracy of the abundance estimation, with the Hadamard-based approach slightly outperforming its Gaussian counterpart.