文章基本信息

标题：Area-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
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作者：Xiawei Chen ; Jing Yu ; Weidong Sun 等
期刊名称：Open Journal of Applied Sciences
印刷版ISSN：2165-3917
电子版ISSN：2165-3925
出版年度：2013
卷号：3
期号：1B
页码：41-46
DOI：10.4236/ojapps.2013.31B009
出版社：Scientific Research Publishing
摘要：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.
关键词：Hyperspectral Image; Spectral Unmixing; Area-Correlation; Bayesian Nonnegative Matrix Factorization