Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation
The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/7/1219 |
| _version_ | 1797571071283560448 |
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| author | Xue Shi Yu Li Quanhua Zhao |
| author_facet | Xue Shi Yu Li Quanhua Zhao |
| author_sort | Xue Shi |
| collection | DOAJ |
| description | The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images. |
| first_indexed | 2024-03-10T20:34:23Z |
| format | Article |
| id | doaj.art-e9bf3ab05a114787bc8d8b55e65961e1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:34:23Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e9bf3ab05a114787bc8d8b55e65961e12023-11-19T21:11:07ZengMDPI AGRemote Sensing2072-42922020-04-01127121910.3390/rs12071219Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image SegmentationXue Shi0Yu Li1Quanhua Zhao2School of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaThe Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images.https://www.mdpi.com/2072-4292/12/7/1219high-resolution remote sensing image segmentationBayesian theoremGaussian mixture model (GMM)hierarchical Gaussian mixture model (HGMM)birth or death Markov chain Monte Carlo (BDMCMC) |
| spellingShingle | Xue Shi Yu Li Quanhua Zhao Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation Remote Sensing high-resolution remote sensing image segmentation Bayesian theorem Gaussian mixture model (GMM) hierarchical Gaussian mixture model (HGMM) birth or death Markov chain Monte Carlo (BDMCMC) |
| title | Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation |
| title_full | Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation |
| title_fullStr | Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation |
| title_full_unstemmed | Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation |
| title_short | Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation |
| title_sort | flexible hierarchical gaussian mixture model for high resolution remote sensing image segmentation |
| topic | high-resolution remote sensing image segmentation Bayesian theorem Gaussian mixture model (GMM) hierarchical Gaussian mixture model (HGMM) birth or death Markov chain Monte Carlo (BDMCMC) |
| url | https://www.mdpi.com/2072-4292/12/7/1219 |
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