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

Yuntao Qian*, Sen Jia, Jun Zhou, Antonio Robles-Kelly

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

519 Citations (Scopus)

Abstract

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 L1 regularizer. Unfortunately, the L1 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 L1/2 sparsity constraint, which we name L1/2 -NMF. The L1/2 regularizer not only induces sparsity but is also a better choice among L q(0 < q < 1) regularizers. We propose an iterative estimation algorithm for L1/2-NMF, which provides sparser and more accurate results than those delivered using the L1 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.

Original languageEnglish
Article number5871318
Pages (from-to)4282-4297
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume49
Issue number11 PART 1
DOIs
Publication statusPublished - Nov 2011
Externally publishedYes

Fingerprint

Dive into the research topics of 'Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization'. Together they form a unique fingerprint.

Cite this