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所属单位：航天学院

发表刊物：DIGITAL SIGNAL PROCESSING

关键字：Hyperspectral unmixing Dictionary-aided unmixing Sparse regression Simultaneous sparse representation

摘要：Dictionary-aided unmixing has been introduced as a semi-supervised unmixing method, under the assumption that the observed mixed pixel of a hyperspectral image can be expressed in the form of different linear combinations of a few spectral signatures from an available spectral library. Sparse regression-based unmixing methods have been recently proposed to solve this problem. Mostly, I-p-norm minimization is a closer surrogate to the l(0)-norm minimization and can be solved more efficiently than l(1)-norm minimization. In this paper, we model the hyperspectral unmixing as a constrained l(2,q)-l(2,p) optimization problem. To effectively solve the induced optimization problems for any q (1 <= q <= 2) and p (0 < p <= 1), an iteratively reweighted least squares algorithm is developed and the convergence of the proposed method is also demonstrated. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method yields better spectral unmixing accuracy in both quantitative and qualitative evaluations than state-of-the-art unmixing algorithms. (C) 2017 Elsevier Inc. All rights reserved.

ISSN号：1051-2004

是否译文：否

发表时间：2018-02-01

合写作者：Bian, Chending,Li, Yunsong,Wang, Keyan

通讯作者：孔繁锵