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Abstract
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.
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
Data are available on the United States Geological Survey website (https://earthexplorer.usgs.gov/).