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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1002/htj.21243

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1433-3058

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Nonnegative matrix factorizationbased hyperspectr

Authors: Zhou Zhang Zhenwei Shi
Publish Date: 2012/07/07
Volume: 23, Issue: 3-4, Pages: 895-905
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Abstract

The fusion of hyperspectral image and panchromatic image is an effective process to obtain an image with both high spatial and spectral resolutions However the spectral property stored in the original hyperspectral image is often distorted when using the class of traditional fusion techniques Therefore in this paper we show how explicitly incorporating the notion of “spectra preservation” to improve the spectral resolution of the fused image First a new fusion model spectral preservation based on nonnegative matrix factorization SPNMF is developed Additionally a multiplicative algorithm aiming at get the numerical solution of the proposed model is presented Finally experiments using synthetic and real data demonstrate the SPNMF is a superior fusion technique for it could improve the spatial resolutions of hyperspectral images with their spectral properties reliably preservedThe work was supported by the National Natural Science Foundation of China under the Grants 60975003 and 91120301 the 973 Program under the Grant 2010CB327904 the open funding project of State Key Laboratory of Virtual Reality Technology and Systems Beihang University Grant No BUAAVR12KF07 the Program for New Century Excellent Talents in University of Ministry of Education of China under the Grant NCET110775 and the Beijing Natural Science Foundation nonnegative component analysis for hyperspectral imagery unmixing under the Grant 4112036

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