Combining Sentinel-1 and Sentinel-2 time series via RNN for object-based land cover classification
| Autor: | Ienco, Dino, Gaetano, Raffaele, Ose, Kenji, Ho Tong Minh, Dinh |
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| Přispěvatelé: | 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) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IEEE International Symposium on Geoscience and Remote Sensing IGARSS IEEE International Geoscience and Remote Sensing Symposium (IGARSS) IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2019, Yokohama, Japan. pp.4881-4884 |
| Popis: | International audience; Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary information provided by radar and optical SITS is still an open challenge. In this context, we propose a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2 (S2) imagery at object level, applied to a real-world land cover classification task. Experiments carried out on the Reunion Island, a overseas department of France in the Indian Ocean, demonstrate the significance of our proposal. |
| Databáze: | OpenAIRE |
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