Spectral-Spatial Hyperspectral Unmixing Using Nonnegative Matrix Factorization

Remotely sensed hyperspectral images contain several bands (at about adjoining frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a significant method for breaking down hyperspectral images into the components (endmembers) that conform each (potentially mixed) pix...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-13
Main Authors:	Zhang, Shaoquan, Zhang, Guorong, Li, Fan, Deng, Chengzhi, Wang, Shengqian, Plaza, Antonio, Li, Jun
Format:	Article
Language:	eng
Subjects:	Blind source separation Blind source separation (BSS) Breaking down Earth surface Factorization Force Hyperspectral imaging hyperspectral unmixing Iterative methods Matrix decomposition nonnegative matrix factorization (NMF) Pixels Remote sensing Sparse matrices Spatial analysis Spatial data Spatial databases spatially weighted unmixing Spectra weighted sparsity
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Summary:	Remotely sensed hyperspectral images contain several bands (at about adjoining frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a significant method for breaking down hyperspectral images into the components (endmembers) that conform each (potentially mixed) pixel and their abundance maps. Nonnegative matrix factorization (NMF) has attracted huge consideration because of the way that it can address mixed pixel scenarios. Most existing NMF unmixing techniques do not include spatial information in the analysis. An ongoing trend is to fuse the spatial and the spectral information contained in hyperspectral scenes to improve the solution. In this article, we build up another hyperspectral unmixing technique named spectral-spatial weighted sparse NMF (SSWNMF), in which two weighting factors are acquainted into the NMF model to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. We adopt a multiplicative iterative strategy to implement the proposed SSWNMF model. Our experimental results, conducted with both synthetic and real hyperspectral data, uncover that the proposed SSWNMF strategy can get accurate unmixing results over those gave by other unmixing strategies, with less parameter tuning.
ISSN:	0196-2892 1558-0644