gaussian mixture model and rjmcmc based rs image segmentation

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ID: 139537
2017
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Abstract
For the image segmentation method based on Gaussian Mixture Model (GMM), there are some problems: 1) The number of component was usually a fixed number, i.e., fixed class and 2) GMM is sensitive to image noise. This paper proposed a RS image segmentation method that combining GMM with reversible jump Markov Chain Monte Carlo (RJMCMC). In proposed algorithm, GMM was designed to model the distribution of pixel intensity in RS image. Assume that the number of component was a random variable. Respectively build the prior distribution of each parameter. In order to improve noise resistance, used Gibbs function to model the prior distribution of GMM weight coefficient. According to Bayes' theorem, build posterior distribution. RJMCMC was used to simulate the posterior distribution and estimate its parameters. Finally, an optimal segmentation is obtained on RS image. Experimental results show that the proposed algorithm can converge to the optimal number of class and get an ideal segmentation results.
Reference Key
shi2017thegaussian Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;X. Shi;Q. H. Zhao
Journal functional & integrative genomics
Year 2017
DOI
10.5194/isprs-archives-XLII-2-W7-647-2017
URL
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/647/2017/isprs-archives-XLII-2-W7-647-2017.pdf
Keywords
civil engineering (general)applied optics technologyengineering (general) photonics

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