Extracting multilayer networks from Sentinel-2 satellite image time series
| Autor: | Danny Lo Seen, Giuseppe Scarpa, Raffaele Gaetano, Roberto Interdonato, Mathieu Roche |
|---|---|
| Přispěvatelé: | Interdonato, R., Gaetano, R., Lo Seen, D., Roche, M., Scarpa, G., Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione [Napoli] (DIETI), Università degli studi di Napoli Federico II, French National Centre for Space Studies (CNES), as part of the AMORIS (Analyse et MOdelisation des Reseaux complexes issus de l'Imagerie Satellitaire) project, APR TOSCA 2019 |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Earth observation
010504 meteorology & atmospheric sciences Sociology and Political Science Social Psychology Computer science Télédétection satellite image time series 0211 other engineering and technologies [SDU.STU]Sciences of the Universe [physics]/Earth Sciences 02 engineering and technology Imagerie par satellite computer.software_genre 01 natural sciences landscape stratification multilayer network remote sensing Sentinel-2 Information discovery Observation satellitaire 021101 geological & geomatics engineering 0105 earth and related environmental sciences business.industry U10 - Informatique mathématiques et statistiques Communication Deep learning Réseaux d'observation terrestre Complex network Information extraction Reference data [SDE]Environmental Sciences Satellite Image Time Series Data mining Artificial intelligence U30 - Méthodes de recherche business computer Analyse de séries chronologiques Network analysis satellite image time serie |
| Zdroj: | Network Science Network Science, Cambridge University Press, 2020, 8 (S1), pp.S26-S42. ⟨10.1017/nws.2019.58⟩ |
| ISSN: | 2050-1250 |
| DOI: | 10.1017/nws.2019.58⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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