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Proceedings Paper

Improved dependent component analysis for hyperspectral unmixing with spatial correlations
Author(s): Yi Tang; Jianwei Wan; Bingchao Huang; Tian Lan
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Paper Abstract

In highly mixed hyerspectral datasets, dependent component analysis (DECA) has shown its superiority over other traditional geometric based algorithms. This paper proposes a new algorithm that incorporates DECA with the infinite hidden Markov random field (iHMRF) model, which can efficiently exploit spatial dependencies between image pixels and automatically determine the number of classes. Expectation Maximization algorithm is derived to infer the model parameters, including the endmembers, the abundances, the dirichlet distribution parameters of each class and the classification map. Experimental results based on synthetic and real hyperspectral data show the effectiveness of the proposed algorithm.

Paper Details

Date Published: 24 November 2014
PDF: 8 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93011A (24 November 2014); doi: 10.1117/12.2071409
Show Author Affiliations
Yi Tang, National Univ. of Defense Technology (China)
Jianwei Wan, National Univ. of Defense Technology (China)
Bingchao Huang, National Univ. of Defense Technology (China)
Tian Lan, National Univ. of Defense Technology (China)
Jinan Univ. (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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