TY - JOUR
T1 - Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization
AU - Qian, Yuntao
AU - Jia, Sen
AU - Zhou, Jun
AU - Robles-Kelly, Antonio
PY - 2011/11
Y1 - 2011/11
N2 - 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.
AB - 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.
KW - Hyperspectral unmixing
KW - nonnegative matrix factorization (NMF)
KW - sparse coding
UR - http://www.scopus.com/inward/record.url?scp=80455174031&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2011.2144605
DO - 10.1109/TGRS.2011.2144605
M3 - Article
SN - 0196-2892
VL - 49
SP - 4282
EP - 4297
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 11 PART 1
M1 - 5871318
ER -