• Journal of Infrared and Millimeter Waves
  • Vol. 33, Issue 5, 552 (2014)
PU Han-Ye1、2、*, WANG Bin1、2, and XIA Wei3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3724/sp.j.1010.2014.00552 Cite this Article
    PU Han-Ye, WANG Bin, XIA Wei. Nonlinear unmixing of hyperspectral imagery based on constrained least squares[J]. Journal of Infrared and Millimeter Waves, 2014, 33(5): 552 Copy Citation Text show less
    References

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    [4] Halimi A, Altmann Y, Dobigeon N, et al.Nonlinear unmixing of hyperspectral images using a generalized bilinear model [J]. IEEE Trans. Geosci. Remote Sens., 2011, 49(11): 4153-4162.

    [5] Altmann Y, Halimi A, Dobigeon N, et al.Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery [J]. IEEE Trans. Image Process., 2012, 21(6): 3017-3025.

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    [7] Nascimento J M P, Dias J M B. Vertex component analysis: A fast algorithm to unmix hyperspectral data [J]. IEEE Trans. Geosci. Remote Sens., 2005, 43(4): 898-910.

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

    [9] Plaza J, Hendrix E M T, Garcia I, et al. On endmember identification in hyperspectral images without pure pixels: A comparison of algorithms [J]. J. Math Imaging Vis., 2012, 42(2-3): 163-1752.

    [10] 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.

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    PU Han-Ye, WANG Bin, XIA Wei. Nonlinear unmixing of hyperspectral imagery based on constrained least squares[J]. Journal of Infrared and Millimeter Waves, 2014, 33(5): 552
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