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Mathematical Problems in Engineering
Volume 2014, Article ID 947453, 11 pages
Research Article

Approximate Sparse Regularized Hyperspectral Unmixing

1Department of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2School of Art & Design, Zhejiang Sci-Tech University, Hangzhou 310018, China

Received 27 January 2014; Revised 18 June 2014; Accepted 19 June 2014; Published 17 August 2014

Academic Editor: Fazal M. Mahomed

Copyright © 2014 Chengzhi Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser than regularizer and much easier to be solved than regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison to regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-art regularizer.