期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 14, 期 -, 页码 5559-5571出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3081984
关键词
Hyperspectral imaging; Convergence; Robustness; Matrix decomposition; Sparse matrices; Optimization; Analytical models; Modified Huber (mHuber); nonnegative matrix factorization (NMF); projected conjugated gradient; tuning parameter
类别
资金
- National Natural Science Foundation of China [61877021]
This article proposes a modified Huber NMF model to achieve robustness to outliers and different kinds of noise in hyperspectral unmixing, with an accelerated convergence rate through replacing updating rules and utilizing a new tuning parameter. Experimental results on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed method compared to state-of-the-art NMF methods in HU.
Hypersepctral unmixing (HU) has been one of the most challenging tasks in hyperspectral image research. Recently, nonnegative matrix factorization (NMF) has shown its superiority in hyperspectral unmixing due to its flexible modeling and little prior requirement. But most NMF algorithms tend to use least square function as the objective, which is sensitive to outliers and different kinds of noise. In this article, we propose a modified Huber (mHuber) NMF model to achieve robustness to outliers and different kinds of noise. Under this robust model, we accelerate the half-quadratic optimization algorithm by replacing multiplicative updating rule with a projected nonlinear conjugated gradient rule, which achieves much faster convergence rate. Moreover, a new tuning parameter, rather than a fixed one, is given to adapt to mHuber loss function. Finally, we perform algorithm analysis and experiments in the synthetic and real-world datasets, which confirms the effectiveness and superiority of the proposed method when compared with several state-of-the-art NMF methods in HU.
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