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

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• 作者：Yan Zhao ; Zhen Zhou ; Donghui Wang ; Yicheng Huang…
• 关键词：hyperspectral image ; unmixing ; nonnegative matrix factorization (NMF) ; correlation ; logarithm function
• 刊名：Frontiers of Optoelectronics
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：9
• 期：4
• 页码：627-632
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Electrical Engineering; Physics, general; Biomedical Engineering;
• 出版者：Higher Education Press
• ISSN：2095-2767
• 卷排序：9

文摘

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.