• Journal of Infrared and Millimeter Waves
  • Vol. 30, Issue 1, 27 (2011)
LIU Xue-Song1、*, WANG Bin1、2, and ZHANG Li-Ming1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: Cite this Article
    LIU Xue-Song, WANG Bin, ZHANG Li-Ming. Hyperspectral unmixing based on nonnegative matrix factorization[J]. Journal of Infrared and Millimeter Waves, 2011, 30(1): 27 Copy Citation Text show less
    References

    [1] Chang C-I. Hyperspectral imaging: techniques for spectral detection and classification[M]. New York: Plenum,2003.

    [2] Keshava N. A survey of spectral unmixing algorithms[J]. Lincoln Lab. J.,2003,14(1): 55—73.

    [3] Li J, Bioucas-Dias J M. Minimum Volume simplex analysis: a fast algorithm to unmix hyperspectral data[C]. Boston: IEEE Geosci. Remote Sens. Symp.,2008,3: 250—253.

    [4] Winter M E. N-find: an algorithm for fast autonomous spectral endmember determination in hyperspectral data[C]. Denver: Proc. of the SPIE conference on imaging spectrometry V,1999,3753: 266—275.

    [5] Nascimento J, Bioucas-Dias J M. Vertex component analysis: a fast algorithm to unmix hyperspectral data[J]. IEEE Trans. Geosci. Remote Sens.,2002,43(4): 898—910.

    [6] Chang C-I, Wu C-C, Liu W, et al. A new growing method for simplex-based endmember extraction algorithm[J]. IEEE Trans. Geosci. Remote Sens.,2006,44(10): 2804—2819.

    [7] Tao X, Wang B, Zhang L. Orthogonal bases approach for decomposition of mixed pixels for hyperspectral imagery[J]. IEEE Geosci. Remote Sens. Lett.,2009,6(2): 219—223.

    [8] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999,401: 788—791.

    [9] Lee D D, Seung H S. Algorithms for non-negative matrix factorization[J]. Adv. Neural Inform. Process Syst.,2000,13: 556—562.

    [10] Miao L, Qi H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J]. IEEE Trans. Geosci. Remote Sens.,2007,45(3): 765—777.

    [11] Jia S, Qian Y. Constrained nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Trans. Geosci. Remote Sens.,2009,47(1): 161—173.

    [12] Kullback S, Leibler R A. On information and sufficiency[J]. The Annals of Math. Stat.,1951,22: 79—86.

    [13] Chang C-I. Spectral information divergence for hyperspectral image analysis[C]. Hamburg: IEEE Geosci. Remote Sens. Symp.,1999,1: 509—511.

    [14] Heinz D C, Chang C-I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Trans. Geosci. Remote Sens.,2001,39(3): 529—545.

    [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 [C/OL]. The 6th Annual JPL Airborne Earth Science Workshop,1996.http: //speclab.cr.usgs.gov/PAPERS.imspec.evol/aviris.evolution.html.

    LIU Xue-Song, WANG Bin, ZHANG Li-Ming. Hyperspectral unmixing based on nonnegative matrix factorization[J]. Journal of Infrared and Millimeter Waves, 2011, 30(1): 27
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