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Extracting multilayer networks from Sentinel-2 satellite image time series

Interdonato Roberto, Gaetano Raffaele, Lo Seen Danny, Roche Mathieu, Scarpa Giuseppe. 2020. Extracting multilayer networks from Sentinel-2 satellite image time series. Network Science, 8 (51) : pp. 526-542.

Journal article ; Article de recherche ; Article de revue à comité de lecture
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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.

Mots-clés Agrovoc : Télédétection, Imagerie par satellite, Analyse de séries chronologiques, Observation satellitaire, Réseaux d'observation terrestre

Mots-clés libres : Remote Sensing, Multilayer network, Satellite Image Time series, Sentinel-2, Landscape stratification

Classification Agris : U30 - Research methods
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : CTS 7 (2019-) - Hors champs stratégiques

Auteurs et affiliations

  • Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA) - auteur correspondant
  • Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)
  • Lo Seen Danny, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0002-7773-2109
  • Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
  • Scarpa Giuseppe, University of Naples Federico II (ITA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/594819/)

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