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: 21 days 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 (21 days 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
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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).

### 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



##### 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.