Extracting multilayer networks from Sentinel-2 satellite image time series
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.
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Subjects: | U30 - Méthodes de recherche, U10 - Informatique, mathématiques et statistiques, télédétection, imagerie par satellite, analyse de séries chronologiques, Observation satellitaire, réseaux d'observation terrestre, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_36761, http://aims.fao.org/aos/agrovoc/c_28778, http://aims.fao.org/aos/agrovoc/c_9000182, http://aims.fao.org/aos/agrovoc/c_9000164, |
Online Access: | http://agritrop.cirad.fr/594819/ http://agritrop.cirad.fr/594819/7/594819.pdf |
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dig-cirad-fr-5948192024-01-29T02:33:38Z http://agritrop.cirad.fr/594819/ http://agritrop.cirad.fr/594819/ Extracting multilayer networks from Sentinel-2 satellite image time series. Interdonato Roberto, Gaetano Raffaele, Lo Seen Danny, Roche Mathieu, Scarpa Giuseppe. 2020. Network Science, 8 (51) : 526-542.https://doi.org/10.1017/nws.2019.58 <https://doi.org/10.1017/nws.2019.58> Extracting multilayer networks from Sentinel-2 satellite image time series Interdonato, Roberto Gaetano, Raffaele Lo Seen, Danny Roche, Mathieu Scarpa, Giuseppe eng 2020 Network Science U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques télédétection imagerie par satellite analyse de séries chronologiques Observation satellitaire réseaux d'observation terrestre http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_28778 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_9000164 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/594819/7/594819.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1017/nws.2019.58 10.1017/nws.2019.58 info:eu-repo/semantics/altIdentifier/doi/10.1017/nws.2019.58 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1017/nws.2019.58 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques télédétection imagerie par satellite analyse de séries chronologiques Observation satellitaire réseaux d'observation terrestre http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_28778 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_9000164 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques télédétection imagerie par satellite analyse de séries chronologiques Observation satellitaire réseaux d'observation terrestre http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_28778 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_9000164 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques télédétection imagerie par satellite analyse de séries chronologiques Observation satellitaire réseaux d'observation terrestre http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_28778 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_9000164 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques télédétection imagerie par satellite analyse de séries chronologiques Observation satellitaire réseaux d'observation terrestre http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_28778 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_9000164 Interdonato, Roberto Gaetano, Raffaele Lo Seen, Danny Roche, Mathieu Scarpa, Giuseppe Extracting multilayer networks from Sentinel-2 satellite image time series |
description |
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. |
format |
article |
topic_facet |
U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques télédétection imagerie par satellite analyse de séries chronologiques Observation satellitaire réseaux d'observation terrestre http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_28778 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_9000164 |
author |
Interdonato, Roberto Gaetano, Raffaele Lo Seen, Danny Roche, Mathieu Scarpa, Giuseppe |
author_facet |
Interdonato, Roberto Gaetano, Raffaele Lo Seen, Danny Roche, Mathieu Scarpa, Giuseppe |
author_sort |
Interdonato, Roberto |
title |
Extracting multilayer networks from Sentinel-2 satellite image time series |
title_short |
Extracting multilayer networks from Sentinel-2 satellite image time series |
title_full |
Extracting multilayer networks from Sentinel-2 satellite image time series |
title_fullStr |
Extracting multilayer networks from Sentinel-2 satellite image time series |
title_full_unstemmed |
Extracting multilayer networks from Sentinel-2 satellite image time series |
title_sort |
extracting multilayer networks from sentinel-2 satellite image time series |
url |
http://agritrop.cirad.fr/594819/ http://agritrop.cirad.fr/594819/7/594819.pdf |
work_keys_str_mv |
AT interdonatoroberto extractingmultilayernetworksfromsentinel2satelliteimagetimeseries AT gaetanoraffaele extractingmultilayernetworksfromsentinel2satelliteimagetimeseries AT loseendanny extractingmultilayernetworksfromsentinel2satelliteimagetimeseries AT rochemathieu extractingmultilayernetworksfromsentinel2satelliteimagetimeseries AT scarpagiuseppe extractingmultilayernetworksfromsentinel2satelliteimagetimeseries |
_version_ |
1792499892388626432 |