SUnGP: A Greedy Sparse Approximation Algorithm for Hyperspectral Unmixing

Spectra measured at a pixel of a remote sensing hyper spectral sensor is usually a mixture of multiple spectra (end-members) of different materials on the ground. Hyper spectral unmixing aims at identifying the end members and their proportions (fractional abundances) in the mixed pixels. Hyper spectral unmixing has recently been casted into a sparse approximation problem and greedy sparse approximation approaches are considered desirable for solving it. However, the high correlation among the spectra of different materials seriously affects the accuracy of the greedy algorithms. We propose a greedy sparse approximation algorithm, called SUnGP, for unmixing of hyper spectral data. SUnGP shows high robustness against the correlation of the spectra of materials. The algorithm employees a subspace pruning strategy for the identification of the end members. Experiments show that the proposed algorithm not only outperforms the state of the art greedy algorithms, its accuracy is comparable to the algorithms based on the convex relaxation of the problem, but with a considerable computational advantage.