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 opensource and freely available on https://github.com/redouanelg/EddyNet.
Lguensat et al. REF proposed a deep learning-based architecture for pixel-wise detection and classification of ocean eddies from the sea surface height (SSH) maps dataset.
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Computer Science Physics