Hyperspectral Unmixing based on Constrained Nonnegative Matrix Factorization via Approximate L0
Authors
Tai Gao, Yang Guo, Chengzhi Deng, Shengqian Wang, Qing Yu
Corresponding Author
Tai Gao
Available Online July 2015.
- 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/).
Cite this article
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 -