Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number
In view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that...
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| Format: | Article |
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Editorial Department of Journal on Communications
2017-02-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017026/ |
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| _version_ | 1841539496751923200 |
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| author | Quan-hua ZHAO Xue SHI Yu WANG Yu LI |
| author_facet | Quan-hua ZHAO Xue SHI Yu WANG Yu LI |
| author_sort | Quan-hua ZHAO |
| collection | DOAJ |
| description | In view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective. |
| format | Article |
| id | doaj-art-3ea5d38f2f48465d8156ca8ec7109bd2 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2017-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-3ea5d38f2f48465d8156ca8ec7109bd22025-01-14T07:11:35ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2017-02-0138344359707102Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class numberQuan-hua ZHAOXue SHIYu WANGYu LIIn view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017026/Gaussian mixture model (GMM)spatially constrainedmaximum likelihood (ML)reversible jump Markov chain Monte Carlo (RJMCMC)remote sensing image segmentation |
| spellingShingle | Quan-hua ZHAO Xue SHI Yu WANG Yu LI Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number Tongxin xuebao Gaussian mixture model (GMM) spatially constrained maximum likelihood (ML) reversible jump Markov chain Monte Carlo (RJMCMC) remote sensing image segmentation |
| title | Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number |
| title_full | Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number |
| title_fullStr | Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number |
| title_full_unstemmed | Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number |
| title_short | Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number |
| title_sort | remote sensing image segmentation based on spatially constrained gaussian mixture model with unknown class number |
| topic | Gaussian mixture model (GMM) spatially constrained maximum likelihood (ML) reversible jump Markov chain Monte Carlo (RJMCMC) remote sensing image segmentation |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017026/ |
| work_keys_str_mv | AT quanhuazhao remotesensingimagesegmentationbasedonspatiallyconstrainedgaussianmixturemodelwithunknownclassnumber AT xueshi remotesensingimagesegmentationbasedonspatiallyconstrainedgaussianmixturemodelwithunknownclassnumber AT yuwang remotesensingimagesegmentationbasedonspatiallyconstrainedgaussianmixturemodelwithunknownclassnumber AT yuli remotesensingimagesegmentationbasedonspatiallyconstrainedgaussianmixturemodelwithunknownclassnumber |