A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery
This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution pro...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tao, Jianbin [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © 2015 Taylor & Francis 2015 |
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of remote sensing - London [u.a.] : Taylor & Francis, 1980, 37(2016), 1, Seite 1 |
|---|---|
| Übergeordnetes Werk: |
volume:37 ; year:2016 ; number:1 ; pages:1 |
| Links: |
|---|
| DOI / URN: |
10.1080/2150704X.2015.1101502 |
|---|
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| 520 | |a This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. | ||
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| 700 | 1 | |a Zhang, Yanbing |4 oth | |
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10.1080/2150704X.2015.1101502 doi PQ20160430 (DE-627)OLC197092232X (DE-599)GBVOLC197092232X (PRQ)i1161-d4ca264db7d88fdcb7f84ef21d2e718eeac7c358fd9a70d286ebd99cadedb8f50 (KEY)0100254620160000037000100001studyofagaussianmixturemodelforurbanlandcovermappi DE-627 ger DE-627 rakwb eng 620 DNB Tao, Jianbin verfasserin aut A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. Nutzungsrecht: © 2015 Taylor & Francis 2015 Shu, Ning oth Wang, Yu oth Hu, Qingwu oth Zhang, Yanbing oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 1 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:1 http://dx.doi.org/10.1080/2150704X.2015.1101502 Volltext http://www.tandfonline.com/doi/abs/10.1080/2150704X.2015.1101502 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 1 |
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10.1080/2150704X.2015.1101502 doi PQ20160430 (DE-627)OLC197092232X (DE-599)GBVOLC197092232X (PRQ)i1161-d4ca264db7d88fdcb7f84ef21d2e718eeac7c358fd9a70d286ebd99cadedb8f50 (KEY)0100254620160000037000100001studyofagaussianmixturemodelforurbanlandcovermappi DE-627 ger DE-627 rakwb eng 620 DNB Tao, Jianbin verfasserin aut A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. Nutzungsrecht: © 2015 Taylor & Francis 2015 Shu, Ning oth Wang, Yu oth Hu, Qingwu oth Zhang, Yanbing oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 1 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:1 http://dx.doi.org/10.1080/2150704X.2015.1101502 Volltext http://www.tandfonline.com/doi/abs/10.1080/2150704X.2015.1101502 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 1 |
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10.1080/2150704X.2015.1101502 doi PQ20160430 (DE-627)OLC197092232X (DE-599)GBVOLC197092232X (PRQ)i1161-d4ca264db7d88fdcb7f84ef21d2e718eeac7c358fd9a70d286ebd99cadedb8f50 (KEY)0100254620160000037000100001studyofagaussianmixturemodelforurbanlandcovermappi DE-627 ger DE-627 rakwb eng 620 DNB Tao, Jianbin verfasserin aut A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. Nutzungsrecht: © 2015 Taylor & Francis 2015 Shu, Ning oth Wang, Yu oth Hu, Qingwu oth Zhang, Yanbing oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 1 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:1 http://dx.doi.org/10.1080/2150704X.2015.1101502 Volltext http://www.tandfonline.com/doi/abs/10.1080/2150704X.2015.1101502 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 1 |
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10.1080/2150704X.2015.1101502 doi PQ20160430 (DE-627)OLC197092232X (DE-599)GBVOLC197092232X (PRQ)i1161-d4ca264db7d88fdcb7f84ef21d2e718eeac7c358fd9a70d286ebd99cadedb8f50 (KEY)0100254620160000037000100001studyofagaussianmixturemodelforurbanlandcovermappi DE-627 ger DE-627 rakwb eng 620 DNB Tao, Jianbin verfasserin aut A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. Nutzungsrecht: © 2015 Taylor & Francis 2015 Shu, Ning oth Wang, Yu oth Hu, Qingwu oth Zhang, Yanbing oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 1 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:1 http://dx.doi.org/10.1080/2150704X.2015.1101502 Volltext http://www.tandfonline.com/doi/abs/10.1080/2150704X.2015.1101502 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 1 |
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10.1080/2150704X.2015.1101502 doi PQ20160430 (DE-627)OLC197092232X (DE-599)GBVOLC197092232X (PRQ)i1161-d4ca264db7d88fdcb7f84ef21d2e718eeac7c358fd9a70d286ebd99cadedb8f50 (KEY)0100254620160000037000100001studyofagaussianmixturemodelforurbanlandcovermappi DE-627 ger DE-627 rakwb eng 620 DNB Tao, Jianbin verfasserin aut A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. Nutzungsrecht: © 2015 Taylor & Francis 2015 Shu, Ning oth Wang, Yu oth Hu, Qingwu oth Zhang, Yanbing oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 1 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:1 http://dx.doi.org/10.1080/2150704X.2015.1101502 Volltext http://www.tandfonline.com/doi/abs/10.1080/2150704X.2015.1101502 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 1 |
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Tao, Jianbin |
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Tao, Jianbin |
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10.1080/2150704X.2015.1101502 |
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study of a gaussian mixture model for urban land-cover mapping based on vhr remote sensing imagery |
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A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery |
| abstract |
This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. |
| abstractGer |
This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. |
| abstract_unstemmed |
This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. |
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A study of a Gaussian mixture model for urban land-cover mapping based on VHR remote sensing imagery |
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http://dx.doi.org/10.1080/2150704X.2015.1101502 http://www.tandfonline.com/doi/abs/10.1080/2150704X.2015.1101502 |
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Shu, Ning Wang, Yu Hu, Qingwu Zhang, Yanbing |
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Shu, Ning Wang, Yu Hu, Qingwu Zhang, Yanbing |
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2024-07-03T17:25:20.579Z |
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