A novel change detection method using remotely sensed image time series value and shape based dynamic time warping
Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huaqiao Xing [verfasserIn] Linye Zhu [verfasserIn] Bingyao Chen [verfasserIn] Liguo Zhang [verfasserIn] Dongyang Hou [verfasserIn] Wenbo Fang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Geocarto International - Taylor & Francis Group, 2023, 37(2022), 25, Seite 9607-9624 |
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| Übergeordnetes Werk: |
volume:37 ; year:2022 ; number:25 ; pages:9607-9624 |
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Link aufrufen |
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| DOI / URN: |
10.1080/10106049.2021.2022013 |
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| Katalog-ID: |
DOAJ099491419 |
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| 520 | |a Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms. | ||
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10.1080/10106049.2021.2022013 doi (DE-627)DOAJ099491419 (DE-599)DOAJ587a4600b4154850a68170c5f3a894bb DE-627 ger DE-627 rakwb eng GB3-5030 Huaqiao Xing verfasserin aut A novel change detection method using remotely sensed image time series value and shape based dynamic time warping 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms. change detection time series dynamic time warping gaussian mixture model Physical geography Linye Zhu verfasserin aut Bingyao Chen verfasserin aut Liguo Zhang verfasserin aut Dongyang Hou verfasserin aut Wenbo Fang verfasserin aut In Geocarto International Taylor & Francis Group, 2023 37(2022), 25, Seite 9607-9624 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:37 year:2022 number:25 pages:9607-9624 https://doi.org/10.1080/10106049.2021.2022013 kostenfrei https://doaj.org/article/587a4600b4154850a68170c5f3a894bb kostenfrei http://dx.doi.org/10.1080/10106049.2021.2022013 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4046 GBV_ILN_4313 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 25 9607-9624 |
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10.1080/10106049.2021.2022013 doi (DE-627)DOAJ099491419 (DE-599)DOAJ587a4600b4154850a68170c5f3a894bb DE-627 ger DE-627 rakwb eng GB3-5030 Huaqiao Xing verfasserin aut A novel change detection method using remotely sensed image time series value and shape based dynamic time warping 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms. change detection time series dynamic time warping gaussian mixture model Physical geography Linye Zhu verfasserin aut Bingyao Chen verfasserin aut Liguo Zhang verfasserin aut Dongyang Hou verfasserin aut Wenbo Fang verfasserin aut In Geocarto International Taylor & Francis Group, 2023 37(2022), 25, Seite 9607-9624 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:37 year:2022 number:25 pages:9607-9624 https://doi.org/10.1080/10106049.2021.2022013 kostenfrei https://doaj.org/article/587a4600b4154850a68170c5f3a894bb kostenfrei http://dx.doi.org/10.1080/10106049.2021.2022013 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4046 GBV_ILN_4313 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 25 9607-9624 |
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A novel change detection method using remotely sensed image time series value and shape based dynamic time warping |
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Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms. |
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Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms. |
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Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms. |
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