期刊
REMOTE SENSING
卷 9, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/rs9101074
关键词
nonnegative matrix factorization; data-guided constraints; sparseness; evenness
类别
资金
- National Nature Science Foundation of China [61571170, 61671408]
- Joint Funds of the Ministry of Education of China [6141A02022314]
Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L-1/2 and L-2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may possess different sparsity levels across locations. The problem remains as to how to impose constraints accordingly when the level of sparsity varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The DGC-NMF imposes on the unknown abundance vector of each pixel with either an L-1/2 constraint or an L-2 constraint according to its estimated mixture level. Experiments on the synthetic data and real hyperspectral data validate the proposed algorithm.
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