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

TL;DR

EddyNet is a deep neural network based on U-Net architecture designed for pixel-wise detection and classification of oceanic eddies from Sea Surface Height maps, improving automated eddy analysis.

Contribution

This paper introduces EddyNet, a novel deep learning model utilizing SELU activation and overlap loss for improved eddy detection from SSH data.

Findings

EddyNet achieves accurate pixel-wise eddy classification.

SELU activation improves training stability over ReLU+BN.

Open-source code and datasets facilitate further research.

Abstract

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.