@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},
	}