Agritrop
Home

DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn

Interdonato Roberto, Ienco Dino, Gaetano Raffaele, Osé Kenji. 2019. DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn. ISPRS Journal of Photogrammetry and Remote Sensing, 149 : pp. 91-104.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
[img] Published version - Anglais
Access restricted to CIRAD agents
Use under authorization by the author or CIRAD.
Interdonato2019.pdf

Télécharger (6MB) | Request a copy

Quartile : Outlier, Sujet : GEOGRAPHY, PHYSICAL / Quartile : Outlier, Sujet : GEOSCIENCES, MULTIDISCIPLINARY / Quartile : Q1, Sujet : REMOTE SENSING / Quartile : Q1, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Abstract : Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, most of machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data. Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning architecture for the analysis of SITS data, namely DuPLO (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Gard site in Mainland France and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal.

Mots-clés Agrovoc : Télédétection, Cartographie de l'occupation du sol, Cartographie de l' utilisation des terres, Analyse d'image, Méthode statistique

Mots-clés géographiques Agrovoc : France, Réunion

Classification Agris : U40 - Surveying methods
U10 - Mathematical and statistical methods
E11 - Land economics and policies

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

Auteurs et affiliations

  • Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA) - auteur correspondant
  • Ienco Dino, IRSTEA (FRA)
  • Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)
  • Osé Kenji, IRSTEA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592819/)

View Item (staff only) View Item (staff only)

[ Page générée et mise en cache le 2020-11-23 ]