期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 19, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3017233
关键词
Hyperspectral imaging; Robustness; Data models; Matrix decomposition; Shape; Loss measurement; General loss; hyperspectral unmixing; nonnegative matrix factorization (NMF)
类别
资金
- National Natural Science Foundation of China [61871177, 41971296, 11771130, 41671342, 11671161]
- Science and Technology Development Fund, Macau SAR [189/2017/A3]
- University of Macau [MYRG2018-00136-FST]
- Zhejiang Provincial Natural Science Foundation of China [LR19D010001]
- Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [18R05]
This letter proposes a general loss-based NMF (GLNMF) model for hyperspectral unmixing. By introducing a robust loss function, the model improves its robustness and achieves higher accuracy compared to existing NMF methods, as demonstrated through experimental results on simulated and real data sets.
Nonnegative matrix factorization (NMF) is a widely used hyperspectral unmixing model which decomposes a known hyperspectral data matrix into two unknown matrices, i.e., endmember matrix and abundance matrix. Due to the use of least-squares loss, the NMF model is usually sensitive to noise or outliers. To improve its robustness, we introduce a general robust loss function to replace the traditional least-squares loss and propose a general loss-based NMF (GLNMF) model for hyperspectral unmixing in this letter. The general loss function is a superset of many common robust loss functions and is suitable for handling different types of noise. Experimental results on simulated and real hyperspectral data sets demonstrate that our GLNMF model is more accurate and robust than existing NMF methods.
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