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Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization

journal contribution
posted on 2011-11-01, 00:00 authored by Yuntao Qian, Sen Jia, Jun Zhou, Antonio Robles-KellyAntonio Robles-Kelly
Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L 1 regularizer. Unfortunately, the L 1 regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the L 1/2 sparsity constraint, which we name L 1/2 -NMF. The L 1/2 regularizer not only induces sparsity but is also a better choice among Lq (0 <; q <; 1) regularizers. We propose an iterative estimation algorithm for L 1/2 -NMF, which provides sparser and more accurate results than those delivered using the L 1 norm. We illustrate the utility of our method on synthetic and real hyperspectral data and compare our results to those yielded by other state-of-the-art methods.

History

Journal

IEEE transactions on geoscience and remote sensing

Volume

49

Issue

11

Pagination

4282 - 4297

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

ISSN

0196-2892

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2011, IEEE