@article{(Open Science Index):https://publications.waset.org/pdf/10007535, title = {Sparse Unmixing of Hyperspectral Data by Exploiting Joint-Sparsity and Rank-Deficiency}, author = {Fanqiang Kong and Chending Bian}, country = {}, institution = {}, abstract = {In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined l2,p-norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy.}, journal = {International Journal of Computer and Information Engineering}, volume = {11}, number = {7}, year = {2017}, pages = {862 - 867}, ee = {https://publications.waset.org/pdf/10007535}, url = {https://publications.waset.org/vol/127}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 127, 2017}, }