EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
This provides a tool for oceanographers and environmental scientists to analyze eddy dynamics, but it is incremental as it adapts existing U-Net methods to a specific domain.
This work tackles the problem of automated detection and classification of oceanic eddies from Sea Surface Height maps using EddyNet, a deep learning architecture, achieving pixel-wise classification into anticyclonic, cyclonic, or non-eddy categories with open-source availability.
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 is a U-Net like network that 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\}. We investigate the use of SELU activation function instead of the classical ReLU+BN and we use an overlap based loss function instead of the cross entropy loss. Keras Python code, the training datasets and EddyNet weights files are open-source and freely available on https://github.com/redouanelg/EddyNet.