Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 647-650, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-647-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 647-650, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-647-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION

X. Shi and Q. H. Zhao X. Shi and Q. H. Zhao
  • Institute for Remote Sensing and Application, School of Geomatics, Liaoning Technical University, Fuxin, Liaoning, 123000, China

Keywords: Gaussian Mixture Model, unknown class, Gibbs function, reversible jump Markov Chain Monte Carlo, Bayes' theorem

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.