Frontiers of Optoelectronics, Volume. 9, Issue 4, 627(2016)
Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization
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 overallcorrelation 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.
Get Citation
Copy Citation Text
Yan ZHAO, Zhen ZHOU, Donghui WANG, Yicheng HUANG, Minghua YU. Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization[J]. Frontiers of Optoelectronics, 2016, 9(4): 627
Category: RESEARCH ARTICLE
Received: May. 5, 2016
Accepted: Oct. 21, 2016
Published Online: Mar. 9, 2017
The Author Email: ZHAO Yan (zh_ao_yan@sina.com)