Hyperspectral Unmixing based on Constrained Nonnegative Matrix Factorization via Approximate L0

DOI

10.2991/icismme-15.2015.194How to use a DOI?

Keywords

Hyperspectral unmixing; AL0-NMF; sparsity; projected gradient.

Abstract

Hyperspectral unmixing is estimating the endmembers and corresponding abundance fractions in a mixed pixel. In the past decade, NMF have been intensively studied to hyperspectral unmixing. As an important constraint for NMF, sparsity could be modeled making use of the L0 regularize. Unfortunately, the L0 regularize is an N-P hard. In this paper, we uses a novel approximate L0 sparsity constraint (which we name AL0-NMF), we propose a project gradient algorithm for AL0 -NMF. The experimental based on synthetic and real data demonstrate the effectiveness of the propose method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tai Gao
AU  - Yang Guo
AU  - Chengzhi Deng
AU  - Shengqian Wang
AU  - Qing Yu
PY  - 2015/07
DA  - 2015/07
TI  - Hyperspectral Unmixing based on Constrained Nonnegative Matrix Factorization via Approximate L0
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 920
EP  - 924
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.194
DO  - 10.2991/icismme-15.2015.194
ID  - Gao2015/07
ER  -