Frontiers of Optoelectronics

, Volume 9, Issue 4, pp 627–632 | Cite as

Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization

  • Yan Zhao
  • Zhen Zhou
  • Donghui Wang
  • Yicheng Huang
  • Minghua Yu
Research Article
  • 31 Downloads

Abstract

The objective function of classical nonnegative matrix factorization (NMF) is non-convexity, which affects the obtaining of optimal solutions. In this paper, we proposed a NMF algorithm, and this algorithm was based on the constraint of endmember spectral correlation minimization and endmember spectral difference maximization. The size of endmember spectral overall-correlation was measured by the correlation function, and correlation function was defined as the sum of the absolute values of every two correlation coefficient between the spectra. In the difference constraint of the endmember spectra, the mutation of matrix trace was slowed down by introducing the natural logarithm function. Combining the image decomposition error with the influences of endmember spectra, in the objective function the projection gradient was used to achieve NMF. The effectiveness of algorithm was verified by the simulated hyperspectral images and real hyperspectral images.

Keywords

hyperspectral image unmixing nonnegative matrix factorization (NMF) correlation logarithm function 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Thouvenin P A, Dobigeon N, Tourneret J Y. Hyperspectral unmixing with spectral variability using a perturbed linear mixing model. IEEE Transactions on Signal Processing, 2016, 64(2): 525–538MathSciNetCrossRefGoogle Scholar
  2. 2.
    Heylen R, Scheunders P. A multilinear mixing model for nonlinear spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 240–251CrossRefGoogle Scholar
  3. 3.
    Zheng C Y, Li H, Wang Q, Chen C L P. Reweighted sparse regression for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 479–488CrossRefGoogle Scholar
  4. 4.
    Altmann Y, Pereyra M, Bioucas-Dias J. Collaborative sparse regression using spatially correlated supports—application to hyperspectral unmixing. IEEE Transactions on Image Processing, 2015, 24(12): 5800–5811MathSciNetCrossRefGoogle Scholar
  5. 5.
    Guillamet D, Vitrià J, Schiele B. Introducing a weighted nonnegative matrix factorization for image classification. Pattern Recognition Letters, 2003, 24(14): 2447–2454CrossRefMATHGoogle Scholar
  6. 6.
    Pauca V P, Piper J, Plemmons R J. Nonnegative matrix factorization for spectral data analysis. Linear Algebra and Its Applications, 2006, 416(1): 29–47MathSciNetCrossRefMATHGoogle Scholar
  7. 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): 765–777CrossRefGoogle Scholar
  8. 8.
    Hoyer P O. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 2004, 5(1): 1457–1469MathSciNetMATHGoogle Scholar
  9. 9.
    Lu X, Wu H, Yuan Y. Double constrained NMF for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2746–2758CrossRefGoogle Scholar
  10. 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)Google Scholar
  11. 11.
    Wu B, Zhao Y, Zhou X. Unmixing mixture pixels of hyperspectral imagery using endmember constrained nonnegative matrix factorization. Computer Engineering, 2008, 34(22): 229–231Google Scholar
  12. 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): 608–619CrossRefGoogle Scholar
  13. 13.
    Heinz D C, Chang C. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529–545CrossRefGoogle Scholar
  14. 14.
    Gillis N, Glineur F. Using underapproximations for sparse nonnegative matrix factorization. Pattern Recognition, 2010, 43(4): 1676–1687CrossRefMATHGoogle Scholar
  15. 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, 1996Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yan Zhao
    • 1
    • 2
  • Zhen Zhou
    • 1
  • Donghui Wang
    • 3
  • Yicheng Huang
    • 4
  • Minghua Yu
    • 4
  1. 1.School of Measurement and CommunicationHarbin University of Science and TechnologyHarbinChina
  2. 2.School of Electrical and Control EngineeringHeilongjiang University of Science and TechnologyHarbinChina
  3. 3.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  4. 4.Qiqihar Vehicle GroupQiqiharChina

Personalised recommendations