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• W4200208557 abstract "This chapter presents an overview of the main time series analysis methods for environment monitoring with earth observation, from classical methods to the deep learning (DL) methods. It summarizes main differences between bi-temporal change detection, annual time series and dense time series analyses, and also presents the three main types of annual time series methods for environment monitoring. The chapter focuses on dense time series methods using all available data, first presenting the main data preprocessing requirements, and provides an overview of the four main types of change detection methods based on dense time series analysis. These include: map classification, trajectory classification, statistical boundary and ensemble approaches. The chapter discusses three kinds of network architectures suited for the analysis of satellite image time series (SITS): recurrent neural networks, convolutional neural networks and hybrid models combining both. It proposes a prospective reflection upon possible convergence at crossroads between SITS analysis, video processing, computer vision and DL." @default.
• W4200208557 created "2021-12-31" @default.
• W4200208557 creator A5006812496 @default.
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• W4200208557 creator A5018231294 @default.
• W4200208557 creator A5023700134 @default.
• W4200208557 date "2021-12-03" @default.
• W4200208557 modified "2023-09-24" @default.
• W4200208557 title "Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond" @default.
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