"Convolutional Neural Networks for the Segmentation of Oceanic Eddies from Altimetric Maps". Preprint can be found here

I already made public some jupyter notebooks and data to let anyone start using it.

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

This is the supplementary material of the publication "EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies", from R. Lguensat et al., accepted as an oral presentation for IGARSS2018. Pre-print at: https://arxiv.org/abs/1711.03954

Eddynet is an U-Net like architecture (a convolutional encoder-decoder followed by a pixel-wise classification layer + skip connections).

• A deep neural net that "emulates" the result of a geometry based and expert based method
• Comparing EddyNet with a version where we use SELU activation function (EddyNet_S). Replacing directly ReLU+BN with SELU resulted in a noisy loss and hurted the performance, we then kept BN after maxpooling, transposed deconvolution and concatenation.
• For this multiclass classification problem, we use (1-mean dice coefficient) as a loss function instead of the categorical cross entropy loss
• Eddynet is easily modulable and can be used for further studies such as adding new information (e.g. Sea Surface Temperature), or training with another ground truth.