24 November 2014 Improved dependent component analysis for hyperspectral unmixing with spatial correlations
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Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93011A (2014) https://doi.org/10.1117/12.2071409
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
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.
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Yi Tang, Yi Tang, Jianwei Wan, Jianwei Wan, Bingchao Huang, Bingchao Huang, Tian Lan, Tian Lan, } "Improved dependent component analysis for hyperspectral unmixing with spatial correlations", 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; https://doi.org/10.1117/12.2071409
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