https://github.com/redouanelg/eddynet

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
https://github.com/redouanelg/eddynet

deep-neural-networks oceanic-eddies segmentation selu u-net

Last synced: about 1 month ago
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EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

• Host: GitHub
• URL: https://github.com/redouanelg/eddynet
• Owner: redouanelg
• License: mit
• Created: 2017-11-10T17:22:50.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-03-25T09:32:31.000Z (about 3 years ago)
• Last Synced: 2025-04-13T06:59:30.822Z (about 1 month ago)
• Topics: deep-neural-networks, oceanic-eddies, segmentation, selu, u-net
• Language: Jupyter Notebook
• Size: 5.34 MB
• Stars: 69
• Watchers: 2
• Forks: 26
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Convolutional Neural Networks for the Segmentation of Oceanic Eddies from Altimetric Maps 

### (Technical report)

"Convolutional Neural Networks for the Segmentation of Oceanic Eddies from Altimetric Maps". Preprint can be found [here](https://www.researchgate.net/publication/328837669_Convolutional_Neural_Networks_for_the_Segmentation_of_Oceanic_Eddies_from_Altimetric_Maps) 

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

# EddyNet 

### (IGARSS conference paper)

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). 


![](unetschema.png)

### Paper main messages:

* 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.

### Some examples of the segmentation

![](example_eddynet.png)



![](example_eddynet2.png)



![](example_eddynet3.png)

##### Note: Like all the Eddy detection algorithms (there is no consensus on the best method to use), the ground truth is not 100% perfect, very few eddies are missed. Still working on an improved dataset and I encourage other researchers to do so.