Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond

Matthieu Molinier, Jukka Miettinen, Dino Ienco, Shi Qiu, Zhe Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientific

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.
Original languageEnglish
Title of host publicationChange Detection and Image Time Series Analysis 2
Subtitle of host publicationSupervised Methods
PublisherWiley-VCH Verlag
Pages109-154
ISBN (Electronic)9781119882299
ISBN (Print)9781789450576
DOIs
Publication statusPublished - 3 Dec 2021
MoE publication typeB2 Part of a book or another research book

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