Electronics Optics & Control, Volume. 26, Issue 1, 38(2019)

Hyperspectral Image Resolution Enhancement Algorithm with Minimum Volume Constraint

WANG Ya-kun1...2, ZHU Rong-gang1,2, LIU Bo1,2 and LI Jian-ru3 |Show fewer author(s)
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  • 1[in Chinese]
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    References(6)

    [3] [3] YOKOYA N, YAIRI T, IWASAKI A.Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion[J].IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(2):528-537.

    [4] [4] ZHANGY F.Spatial resolution enhancement of hyperspectral image based on the combination of spectral mixing model and observation model[C]//Proceedings of SPIE-the International Society for Optical Engineering, 2014:201-204.

    [5] [5] LIU D, BOUFOUNOS P T.Dictionary learning based pan-sharpening[C]//IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012:2397-2400.

    [6] [6] LEE D D, SEUNG H S.Learning the parts of objects by non-negative matrix factorization [J].Nature, 1999, 401(6755):788-791.

    [8] [8] HOYER P O.Non-negative matrix factorization with sparseness constraints[J].Journal of Machine Learning Research, 2004, 5(9):1457-1469.

    [10] [10] WEI Q, BIOUCAS-DIA J M, DOBIGEON N, et al.Fusion of multispectral and hyperspectral images based on sparse representation[C]//The 22nd European Signal Processing Conference(EUSIPCO), 2014:1-5.

    CLP Journals

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    WANG Ya-kun, ZHU Rong-gang, LIU Bo, LI Jian-ru. Hyperspectral Image Resolution Enhancement Algorithm with Minimum Volume Constraint[J]. Electronics Optics & Control, 2019, 26(1): 38

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    Paper Information

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    Received: Jul. 16, 2018

    Accepted: --

    Published Online: Jan. 19, 2019

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2019.01.009

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