• Frontiers of Optoelectronics
  • Vol. 9, Issue 4, 627 (2016)
Yan ZHAO1、2、*, Zhen ZHOU1, Donghui WANG3, Yicheng HUANG4, and Minghua YU4
Author Affiliations
  • 1School of Measurement and Communication, Harbin University of Science and Technology, Harbin 150080, China
  • 2School of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
  • 3College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • 4Qiqihar Vehicle Group, Qiqihar 161000, China
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    DOI: 10.1007/s12200-016-0647-7 Cite this Article
    Yan ZHAO, Zhen ZHOU, Donghui WANG, Yicheng HUANG, Minghua YU. Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization[J]. Frontiers of Optoelectronics, 2016, 9(4): 627 Copy Citation Text show less
    References

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    [2] Heylen R, Scheunders P. A multilinear mixing model for nonlinear spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 240–251

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    [5] Guillamet D, Vitrià J, Schiele B. Introducing a weighted non-negative matrix factorization for image classi.cation. Pattern Recognition Letters, 2003, 24(14): 2447–2454

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    [7] Miao L, Qi H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45 (3): 765–777

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    [9] Lu X, Wu H, Yuan Y. Double constrained NMF for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2746–2758

    [10] Luo W F, Zhong L, Zhang B, Gao L R. Independent component analysis for spectral unmixing in hyperspectral remote sensing image. Spectroscopy and Spectral Analysis, 2010, 30(6): 1628– 1633 (in Chinese)

    [11] Wu B, Zhao Y, Zhou X. Unmixing mixture pixels of hyperspectral imagery using endmember constrained nonnegative matrix factor-ization. Computer Engineering, 2008, 34(22): 229–231

    [12] Chang C, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608–619

    [13] Heinz D C, Chang C. Fully constrained least squares linear spectral mixture analysis method for material quanti.cation in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529–545

    [14] Gillis N, Glineur F. Using underapproximations for sparse nonnegative matrix factorization. Pattern Recognition, 2010, 43 (4): 1676–1687

    [15] Clark R N, Swayze G A. Evolution in imaging spectroscopy analysis and sensor signal-to-noise: an examination of how far we have come. In: Proceedings of The 6th Annual JPL Airborne Earth Science Workshop, 1996

    Yan ZHAO, Zhen ZHOU, Donghui WANG, Yicheng HUANG, Minghua YU. Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization[J]. Frontiers of Optoelectronics, 2016, 9(4): 627
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