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M. N. Schmidt, “Linearly Constrained Bayesian Matrix Factorization for Blind Source Separation,” Advances in Neural Information Processing Systems, 2009.22: p. 1624-1632.

has been cited by the following article:

• TITLE: Area-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization

  AUTHORS: Xiawei Chen, Jing Yu, Weidong Sun

  KEYWORDS: Hyperspectral Image; Spectral Unmixing; Area-Correlation; Bayesian Nonnegative Matrix Factorization

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.3 No.1B, January 22, 2013

  ABSTRACT: To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed me-thod, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of the adjacent areas are used to estimate the priori probability density func-tions of the endmembers in the current area, which works as a type of constraint in the iterative spectral unmixing process. Experimental results demonstrate the effectivity and efficiency of the proposed method both for synthetic and real hyperspectral images, and it can provide a useful tool for spatial correlation and comparation analysis between ad-jacent or similar areas.