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

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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.

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Author information

Authors and Affiliations

  1. School of Measurement and Communication, Harbin University of Science and Technology, Harbin, 150080, China

    Yan Zhao & Zhen Zhou

  2. School of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China

    Yan Zhao

  3. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Donghui Wang

  4. Qiqihar Vehicle Group, Qiqihar, 161000, China

    Yicheng Huang & Minghua Yu

Authors
  1. Yan Zhao
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  2. Zhen Zhou
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  3. Donghui Wang
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  4. Yicheng Huang
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  5. Minghua Yu
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Corresponding author

Correspondence to Yan Zhao.

Additional information

Yan Zhao graduated from Harbin University of Science and Technology with the master degree in 2007. Currently she is a Ph.D. candidate in Harbin University of Science and Technology, and she is a lecturer in Heilongjiang University of Science and Technology. Her research interests focus on image processing and intelligent control.

Zhen Zhou graduated from School of Measurement and Communication, Harbin University of Science and Technology with the Ph.D. degree in 2005. Currently he is a professor in Harbin University of Science and Technology. His research interests focus on biological information detection.

Donghui Wang graduated from College of Information and Communication Engineering, Harbin Engineering University with the master degree in 2007. Currently he is a Ph.D. candidate in Harbin Engineering University. His research interests focus on image processing.

Yicheng Huang received his bachelor degree from Jiamusi University in 2001. Currently he is an engineer in Qiqihar Vehicle Group. His research interests focus on image processing and intelligent control.

Minghua Yu received her bachelor degree from Jiamusi University in 2001. Currently she is a lecturer in Qiqihar Vehicle Group. Her research interests focus on image processing and intelligent control.

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Zhao, Y., Zhou, Z., Wang, D. et al. Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization. Front. Optoelectron. 9, 627–632 (2016). https://doi.org/10.1007/s12200-016-0647-7

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  • DOI: https://doi.org/10.1007/s12200-016-0647-7

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