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Remote Sensing Technology and Application  2015, Vol. 30 Issue (1): 82-91    DOI: 10.11873/j.issn.1004-0323.2015.1.0082
    
Fusion of Hyperspectral and Multispectral Data Using Nonnegative Matrix Factorization
Cui Yanrong1,He Binbin1,Zhang Ying1,Li Man2
(1.University of Electronic Science and Technology of China,School of
Resource and Environment,Chengdu 611731,China;
2.E-government Center of Environmental Protection Office in Guizhou Province,Guiyang 550000,China)
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Abstract  

The hyperspectral data has very high spectral resolution,but low spatial resolution with the trade\|off between the spectral and spatial resolution,as well as the physical limits.Therefore,in many cases,the spatial resolution of the hyperspectral image system is lower than the multispectral image system that has the less spectral channels.The fusion of the hyperspectral and multispectral data can produce fused data with high spatial and spectral resolution which can contribute to the identification and classification of the material at a finer spatial resolution.NMF (Nonnegative Matrix Factorization) algorithm is used to perform the fusion of low spatial resolution hyperspectral data and high spatial resolution multispectral data.Firstly,VCA (Vertex Component Analysis) is used to perform the factorization of hyperspectral data to obtain endmember spectra matrix and abundance matrix; Secondly,NMF(Nonnegative Matrix Factorization) is used to unmix the hyperspectral and multispectral data alternatively to obtain high spectral resolution endmember spectra matrix W and high spatial resolution abundance matrix H.Finally,fusion data with high spectral resolution and high spatial resolution can be obtained by multiplying the two matrices,.Sensor observation models of the data are built in the initialization matrix of each NMF unmixing procedure.The experiments with AVIRIS data and HJ-1A data have shown that NMF method can be used to improve the spatial resolution on all wavelength regions,and the higher qualities of the estimated data by NMF can be used for the classification of the materials and identification at a finer spatial resolution.

Key words:  Nonnegative matrix factorization      Hyperspectral      Multispectral      Fusion     
Received:  05 January 2014      Published:  11 March 2015
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Cui Yanrong
He Binbin
Zhang Ying
Li Man

Cite this article: 

Cui Yanrong,He Binbin,Zhang Ying,Li Man. Fusion of Hyperspectral and Multispectral Data Using Nonnegative Matrix Factorization. Remote Sensing Technology and Application, 2015, 30(1): 82-91.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2015.1.0082     OR     http://www.rsta.ac.cn/EN/Y2015/V30/I1/82

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