Abstract
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.
Highlights
GMM was designed to model the distribution of pixel intensity in RS image
Experimental results show that, the proposed algorithm can converge to the optimal number of class and get an ideal segmentation results
This paper proposed a RS image segmentation method that combining GMM with Reversible jump Markov Chain Monte Carlo (RJMCMC)
Summary
Image segmentation is one of the important steps in the image processing. The good segmentation result has an important influence on other works in image processing(Drǎgut, 2010). Because of traditional GMM only uses pixel gray information, and without the pixel space location information (Blekas, 2005) This segmentation method is extremely sensitive to image noise. Reversible jump Markov Chain Monte Carlo (RJMCMC) method (Kato, 2006; Zhang, 2004.) is widely used to image segmentation to estimate the number of classes. GMM was designed to model the distribution of pixel intensity in RS image. In order to improve noise resistance, used Gibbs function to model the prior distribution of GMM weight n p X | w,θ f xi | Π ,θ i 1.
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