NONNEGATIVE MATRIX FACTORIZATION WITH DATA-GUIDED CONSTRAINTS

被引：0

作者：

Huang, Risheng ^{[1
]}

Li, Xiaorun ^{[1
]}

Zhao, Liaoying ^{[2
]}

机构：

[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

[2] Hangzhou Dianzi Univ, Inst Comp Applicat Technol, Hangzhou 310018, Zhejiang, Peoples R China

来源：

2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年

关键词：

Nonnegative matrix factorization; data guided constrains; sparseness; smoothness;

D O I：

暂无

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Hyperspectral unmixing aims to estimate a set of endmembers and their corresponding percentages in pixels. NMF and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 regularizer and L2 regularizer can be added into NMF to enforce sparseness and smoothness respectively. In practice, an rigion in hyperspectral image may possesses different sparse level across locations. It remains a problem how to impose constraints accordingly when the level of sparse varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The UGC-NMF assigns sparseness or smoothness constraints on abundance of each pixel individually according to their mixed level. Experiments on the synthetic data validate the proposed algorithm.

引用

页码：590 / 593

页数：4

共 9 条

[1] Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [J].

Bioucas-Dias, Jose M. ;

Plaza, Antonio ;

Dobigeon, Nicolas ;

Parente, Mario ;

Du, Qian ;

Gader, Paul ;

Chanussot, Jocelyn .

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (02) :354-379

[2]

Hoyer PO, 2004, J MACH LEARN RES, V5, P1457

[3] Learning the parts of objects by non-negative matrix factorization [J].

Lee, DD ;

Seung, HS .

NATURE, 1999, 401 (6755) :788-791

[4] Manifold Regularized Sparse NMF for Hyperspectral Unmixing [J].

Lu, Xiaoqiang ;

Wu, Hao ;

Yuan, Yuan ;

Yan, Pingkun ;

Li, Xuelong .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (05) :2815-2826

[5] Nonnegative matrix factorization for spectral data analysis [J].

Pauca, V. Paul ;

Piper, J. ;

Plemmons, Robert J. .

LINEAR ALGEBRA AND ITS APPLICATIONS, 2006, 416 (01) :29-47

[6] Hyperspectral Unmixing via L1/2 Sparsity-Constrained Nonnegative Matrix Factorization [J].

Qian, Yuntao ;

Jia, Sen ;

Zhou, Jun ;

Robles-Kelly, Antonio .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (11) :4282-4297

[7]

Tarabalka Y., 1993, B AM ASTRONOMICAL SO, V25, P1033

[8] An Endmember Dissimilarity Constrained Non-Negative Matrix Factorization Method for Hyperspectral Unmixing [J].

Wang, Nan ;

Du, Bo ;

Zhang, Liangpei .

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (02) :554-569

[9]

Yuntao Qian, 2010, Proceedings 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2010), P447, DOI 10.1109/DICTA.2010.82

← 1 →