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

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.

References in corpus (5)

• Deep learning in remote sensing: a review
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Self-Normalizing Neural Networks
• Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
• Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

Cited by in corpus (8)

• Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
• Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks
• Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
• Extract and Characterize Hairpin Vortices in Turbulent Flows
• Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
• SymmetricNet: A mesoscale eddy detection method based on multivariate fusion data
• Predicting the flow field in a U-bend with deep neural networks
• A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images