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Extracting multilayer networks from Sentinel-2 satellite image time series

Published online by Cambridge University Press:  17 January 2020

Roberto Interdonato Open the ORCID record for Roberto Interdonato [Opens in a new window] ,
Raffaele Gaetano ,
Danny Lo Seen ,
Mathieu Roche  and
Giuseppe Scarpa
Roberto Interdonato*
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Raffaele Gaetano
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Danny Lo Seen
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Mathieu Roche
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Giuseppe Scarpa
Affiliation:
Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125Naples, Italy (e-mail: giscarpa@unina.it)
*
*Corresponding author. Email: roberto.interdonato@cirad.fr
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Abstract

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

Keywords

Hocine CheriUniversity of BurgundyFrancemultilayer networkremote sensingsatellite image time seriesSentinel-2landscape stratification
Type
Research Article
Information
Network Science , Volume 8 , Issue S1: Complex Networks 2018 , July 2020 , pp. S26 - S42
Copyright
© Cambridge University Press 2020

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