Work

Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization

Year: 2011

Type: article

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$L_{1}$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$L_{q}(0 < q < 1)$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.

Source: IEEE Transactions on Geoscience and Remote Sensing

DOI: 10.1109/tgrs.2011.2144605

Authors: Yuntao Qian, Sen Jia, Jun Zhou, Antonio Robles-Kelly

Institutions: Zhejiang University, Shenzhen University, Australian National University, Data61

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Australian Government, National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Australian Research Council

Award: 高空间分辨多光谱数据辅助的高光谱解混概率图模型研究