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GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION
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| Zeitschriftentitel: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , |
| In: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7, 2017, S. 647-650 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Shi, X. Zhao, Q. H. Shi, X. Zhao, Q. H. |
|---|---|
| author |
Shi, X. Zhao, Q. H. |
| spellingShingle |
Shi, X. Zhao, Q. H. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION General Earth and Planetary Sciences General Environmental Science |
| author_sort |
shi, x. |
| spelling |
Shi, X. Zhao, Q. H. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-647-2017 <jats:p>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. </jats:p> GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| title |
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_unstemmed |
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_full |
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_fullStr |
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_full_unstemmed |
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_short |
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_sort |
gaussian mixture model and rjmcmc based rs image segmentation |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-647-2017 |
| publishDate |
2017 |
| physical |
647-650 |
| description |
<jats:p>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.
</jats:p> |
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| container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| description | <jats:p>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. </jats:p> |
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| imprint_str_mv | Copernicus GmbH, 2017 |
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| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| spelling | Shi, X. Zhao, Q. H. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-647-2017 <jats:p>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. </jats:p> GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Shi, X., Zhao, Q. H., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION, General Earth and Planetary Sciences, General Environmental Science |
| title | GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_full | GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_fullStr | GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_full_unstemmed | GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_short | GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| title_sort | gaussian mixture model and rjmcmc based rs image segmentation |
| title_unstemmed | GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-647-2017 |