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


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.


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


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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

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