Image and Signal Processing Methods

Application of Gibbs sampling in efficient hyperspectral unmixing based on the mixtures of Dirichlet components

[+] Author Affiliations
Solmaz Babakan, Majid Mohammady Oskouei

Sahand University of Technology, Faculty of Mining Engineering, Sahand New Town, Tabriz 5168637166, Iran

J. Appl. Remote Sens. 9(1), 095045 (Nov 06, 2015). doi:10.1117/1.JRS.9.095045
History: Received March 18, 2015; Accepted October 7, 2015
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Abstract.  The independent component analysis has been commonly employed in hyperspectral unmixing. However, the success of this method is highly dependent on the independency of its sources assumption. Dependent component analysis (DECA) algorithm, which utilizes a Dirichlet mixture model, was developed to provide more adequate spectral unmixing of dependent sources. Estimation of the unknown model parameters using the expectation maximization algorithm in DECA resulted in some insufficiencies. DECAGibbs algorithm is introduced to improve unmixing accuracy by applying the Gibbs sampling method to the parameter estimation process of DECA, which is conducted in different manners of modeling the observations. Functionality of the DECAGibbs algorithm is examined through the artificial datasets and an AVIRIS image of Cuprite, Nevada, indicating better decomposition of mixed observations. Finally, the best performing model was employed in mineralogical mapping of the Lahroud region, northwest Iran, by a Hyperion image. The results represent the high reliability of the proposed method according to the geological studies of the area. Since the practical application of the mixture models relies upon the efficient estimation of their involved parameters, the performance of the DECA algorithm is improved by employing the Bayesian parameter estimation approaches in this research.

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Citation

Solmaz Babakan and Majid Mohammady Oskouei
"Application of Gibbs sampling in efficient hyperspectral unmixing based on the mixtures of Dirichlet components", J. Appl. Remote Sens. 9(1), 095045 (Nov 06, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095045


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