Attentive spatial temporal graph CNN for land cover mapping from multi temporal remote sensing data

Censi Alessandro Michele, Ienco Dino, Gbodjo Yawogan Jean Eudes, Pensa Ruggero Gaetano, Interdonato Roberto, Gaetano Raffaele. 2021. Attentive spatial temporal graph CNN for land cover mapping from multi temporal remote sensing data. IEEE Access, 9:20324283 : 23070-23082.

https://doi.org/10.1109/ACCESS.2021.3055554

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Quartile : Q2, Sujet : COMPUTER SCIENCE, INFORMATION SYSTEMS / Quartile : Q2, Sujet : ENGINEERING, ELECTRICAL & ELECTRONIC / Quartile : Q2, Sujet : TELECOMMUNICATIONS

Résumé : Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the spatial patterns that characterize these data structures. Focusing on land cover classification (or mapping) tasks, the majority of approaches dealing with SITS data only considers the temporal dimension, while the integration of the spatial context is frequently neglected. In this work, we propose an attentive spatial temporal graph convolutional neural network that exploits both spatial and temporal dimensions in SITS. Despite the fact that this neural network model is well suited to deal with spatio-temporal information, this is the first work that considers it for the analysis of SITS data. Experiments are conducted on two study areas characterized by different land cover landscapes and real-world operational constraints (i.e., limited labeled data due to acquisition costs). The results show that our model consistently outperforms all the competing methods obtaining a performance gain, in terms of F-Measure, of at least 5 points with respect to the best competing approaches on both benchmarks.

Mots-clés Agrovoc : cartographie de l'occupation du sol, cartographie de l'utilisation des terres, modélisation, imagerie par satellite, télédétection, analyse de séries chronologiques, satellite d'observation de la Terre, données spatiales

Mots-clés libres : Deep Learning, Sentinel-2, Série temporelle d'images satellites

Classification Agris : U30 - Méthodes de recherche
P01 - Conservation de la nature et ressources foncières

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

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