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Communication Dans Un Congrès Année : 2018

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

Résumé

This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet consists of a convolutional encoder-decoder followed by a pixel-wise classification layer. The output is a map with the same size of the input where pixels have the following labels {'0': Non eddy, '1': anticyclonic eddy, '2': cyclonic eddy}. Keras Python code, the training datasets and EddyNet weights files are open-source and freely available on https://github.com/redouanelg/EddyNet.

Dates et versions

hal-01929509 , version 1 (03-12-2018)

Identifiants

Citer

Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, et al.. EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies. International Geoscience and Remote Sensing Symposium (IGARSS 2018), Jul 2018, Valence, Spain. pp.1764-1767, ⟨10.1109/IGARSS.2018.8518411⟩. ⟨hal-01929509⟩
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