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2020 | OriginalPaper | Chapter

Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images

Authors : Michelle Aubrun, Andres Troya-Galvis, Mohanad Albughdadi, Romain Hugues, Marc Spigai

Published in: Environmental Software Systems. Data Science in Action

Publisher: Springer International Publishing

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Abstract

The recent popularity of artificial intelligence techniques and the wealth of free and open access Copernicus data have led to the development of new data analytics applications in the Earth Observation domain. Among them, is the detection of changes on image time series, and in particular, the estimation of levels and superficies of changes. In this paper, we propose an unsupervised framework to detect generic but relevant and reliable changes using pairs of Sentinel-2 images. To illustrate this method, we will present a scenario focusing on the detection of changes in vineyards due to natural hazards such as frost and hail.

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Metadata
Title
Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images
Authors
Michelle Aubrun
Andres Troya-Galvis
Mohanad Albughdadi
Romain Hugues
Marc Spigai
Copyright Year
2020
Book
Environmental Software Systems. Data Science in Action

Print ISBN: 978-3-030-39814-9

Electronic ISBN: 978-3-030-39815-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39815-6_1

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