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Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture

Ienco Dino, Interdonato Roberto, Gaetano Raffaele, Ho Tong Minh Dinh. 2019. Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture. ISPRS Journal of Photogrammetry and Remote Sensing, 158 : pp. 11-22.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Quartile : Outlier, Sujet : GEOGRAPHY, PHYSICAL / Quartile : Outlier, Sujet : GEOSCIENCES, MULTIDISCIPLINARY / Quartile : Q1, Sujet : REMOTE SENSING / Quartile : Q1, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Abstract : The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal.

Mots-clés Agrovoc : Cartographie de l'occupation du sol, Analyse de séries chronologiques, Imagerie, Imagerie par satellite, Observation satellitaire

Mots-clés géographiques Agrovoc : Réunion, Burkina Faso

Mots-clés complémentaires : deep learning

Mots-clés libres : Deep Learning, Occupation du sol, Sentinel-1, Sentinel-2

Classification Agris : U30 - Research methods
U10 - Computer science, mathematics and statistics
P31 - Soil surveys and mapping

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Auteurs et affiliations

  • Ienco Dino, IRSTEA (FRA) - auteur correspondant
  • Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA)
  • Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)
  • Ho Tong Minh Dinh, IRSTEA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/597770/)

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