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

arXiv:1711.03954v1 [] 10 Nov 2017

Redouane Lguensat, Member, IEEE, Miao Sun, Ronan Fablet, Senior Member, IEEE, Evan Mason, Pierre Tandeo,
and Ge Chen

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
consists of a convolutional encoder-decoder followed by a pixelwise 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}. Keras Python code,
the training datasets and EddyNet weights files are open-source
and freely available on https://github.com/redouanelg/EddyNet.
Index Terms—Mesoscale eddy, Segmentation, Classification,
Deep learning, Convolutional Neural Networks.

I. I NTRODUCTION
OING ”deeper” with artificial neural networks (ANNs)
by using more than the original three layers (input,
hidden, output) started the so-called deep learning era. The
developments and discoveries which are still ongoing are
producing impressive results and reaching state-of-the-art performances in various fields. The reader is invited to read
[1] for a general introduction to deep learning. In particular,
Convolutional Neural Networks (CNN) sparked-off the deep
learning revolution in the image processing community and are
now ubiquitous in computer vision applications. This has led
numerous researchers from the remote sensing community to
investigate the use of this powerful tool for tasks like object
recognition, scene classification, etc... More applications of
deep learning for remote sensing data can be found in [2], [3]
and references therein.
By standing on the shoulders of recent achievements in deep
learning for image segmentation we present ”EddyNet”, a deep
neural network for automated eddy detection and classification
from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (hereinafter
denoted by AVISO-SSH). EddyNet is inspired by ideas from
widely used image segmentation architectures, in particular Ushaped architectures such as U-Net [4]. We investigate the use
of Scaled Exponential Linear Units (SELU) [5], [6] instead of
the classical ReLU + Batch Normalization (R+BN) and show
that we greatly speed up the training process while reaching

G

R. Lguensat and R. Fablet and P. Tandeo are with IMT Atlantique;
UBL; Lab-STICC; 29200 Brest, France. E-mail: redouane.lguensat@imtatlantique.fr.
M. Sun is with the National Marine Data and Information Service; Key
Laboratory of Digital Ocean; 300171 Tianjing, China.
G. Chen is with Department of Marine Information Technology; College
of Information Science and Engineering; Ocean University of China; 266000
Qingdao, China.
E. Mason is with the Mediterranean Institute for Advanced Studies
(IMEDEA/CSIC-UIB), 07190 Esporles - Balearic Islands, Spain.

comparable results. We adopt a loss function based on the
Dice coefficient (also known as the F1 measure) and illustrate
that we reach better scores for the two most relevant classes
(cyclonic and anticyclonic) than with using the categorical
cross-entropy loss. We also supplement dropout layers to our
architecture that prevents EddyNet from overfitting.
Our work joins the emerging cross-fertilization between
the remote sensing and machine learning communities that
is leading to significant contributions in addressing the segmentation of remote sensing images [7]–[9]. To the best of
our knowledge, the present work is the first to propose a
deep learning based architecture for pixel-wise classification
of eddies, dealing with the challenges of this particular type
of data.
This letter is organized as follows: Section II presents the
eddy detection and classification problem and related work.
Section III describes the data preparation process. Section IV
presents the architecture of EddyNet and details the training
process. Section V reports the different experiments considered in this work and discusses the results. Our conclusion
and future work directions are finally stated in Section VI.
II. P ROBLEM STATEMENT AND RELATED WORK
Ocean mesoscale eddies can be defined as rotating water
masses, they are omnipresent in the ocean and carry critical
information about large-scale ocean circulation [10], [11].
Eddies transport different relevant physical quantities such as
carbon, heat, phytoplankton, salt, etc. This movement helps in
regulating the weather and mixing the ocean [12]. Detecting
and studying eddies helps also considering their effects in
ocean climate models [13]. With the development of altimeter
missions and since the availability of two or more altimeters
at the same time, merged products of Sea Surface Height
(SSH) reached a sufficient resolution to allow the detection of
mesoscale eddies [14], [15]. SSH maps allow us distinguish
two classes of eddies: i) anticyclonic eddies that are recognized
by their positive SLA (Sea Level Anomaly which is SSH
anomaly with regard to a given mean) and ii) cyclonic eddies
that are characterized by their negative SLA.
In recent years, several studies were conducted with the aim
of detecting and classifying eddies in an automated fashion
[16]. Two major families of methods prevail in the literature,
namely, physical parameter-based methods and geometrical
contour-based methods. The most popular representative of
physical parameter-based methods is the Okubo-Weiss parameter method [17], [18]. The Okubo-Weiss parameter method
is however criticized for its expert-based and region-specific

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Fig. 1: A snapshot of a SSH map from the Southern Atlantic
Ocean with the detected eddies by PET14 algorithm, red
shapes represent anticyclonic eddies while green shapes are
cyclonic eddies

parameters and also for its sensitivity to noisy SSH maps
[19]. Other methods were since then developed using other
techniques such as wavelet decomposition [20], winding angle
[21], etc. Geometric-based methods rely on considering the
eddies as elliptic shapes and use closed contour techniques,
the most popular method remains Chelton et al. method
[11] (hereinafter called CSS11). Methods that combines ideas
from both worlds are called hybrid methods (e.g. [22], [23]).
Machine learning methods were also used in the past to
propose a solution to the problem [24], [25], recently they
are again getting an increasing attention [26], [27].
We propose in this work to benefit from the advances in
deep learning to address ocean eddy detection and classification. Our proposed deep learning based method requires
a training database consisting of SSH maps and their corresponding eddy detection and classification results. In this
work, we train our deep learning methods from the results of
the py-eddy-tracker SSH-based approach (hereinafter PET14)
[28], the algorithm developed by Mason et al. is closely related
to CSS11 but has some significant differences such as not
allowing multiple local extremum in an eddy. An example
of a PET14 result is given in Figure 1 which shows eddies
identified in the southwest Atlantic (see [29]). The outputs
of the eddy tracker algorithm provide the center coordinates
of each classified eddy along with its speed and effective
contours. Since we aim for a pixelwise classification, i.e., each
pixel is classified, we transform the outputs into segmentation
maps such as the example shown in Figure 2. We consider here
the speed contour which corresponds to the closed contour that
has the highest mean geostrophic rotational current. The speed
contour can be seen as the most energetic part of the eddy and
is usually smaller than the effective radius. The next section
describes further the data preparation process that yields the
training database of pixelwise classification maps.
III. DATA PREPARATION
As stated in the previous section, we consider PET14 outputs as a training database for our deep-neural-network based
algorithms. We use 15 years (1998-2012) of daily detected
and classified eddies. The corresponding SSH maps (AVISOSSH) are provided by the Copernicus Marine Environment

Fig. 2: Example of a SSH-Segmentation training couple,
anticyclonic (green), cyclonic (brown), non eddy (blue)
Monitoring Service (CMEMS). The resolution of the SSH
maps is 0.25◦ .
Due to memory constraints, the input image of our architectures is 128 × 128 pixels. The first 14 years are used as a
training dataset and the last year (2012) is left aside for testing
our architecture. We consider the Southern Atlantic Ocean
region depicted in Figure 1 and cut the top region where no
eddies were detected. Then we randomly sample one 128×128
patch from each SSH map, which leaves us with 5100 training
samples. A significant property of this type of data is that
its dynamics are slow, a single eddy can live for several
days or even more than a year. In addition to the fact that
a 128 × 128 patch can comprise several examples of cyclonic
and anticyclonic eddies, we believe that data augmentation
(adding rotated versions of the patches to the training database
for example) is not needed; we observed experiments (not
shown here) that even resulted in performance degradation.
The next step consists of extracting the SSH 128×128 patches
from AVISO-SSH. For land pixels or regions with no data
we replaced the standard fill value by a zero; this helps to
avoid outliers and does not affect detection since eddies are
located in regions with non zero SSH. The final and essential
step is the creation of the segmentation masks of the training
patches. This is done by creating polygon shapes using the
speed contour coordinates mapped onto the nearest lattices
in the AVISO-SSH 0.25◦ grid. Pixels inside each polygon
are then labeled with the class of the polygon representing
the eddy {’0’: Non eddy/land/no data, ’1’: anticyclonic eddy,
’2’: cyclonic eddy}. An example of the coupled {SSH map,
segmentation map} from the training dataset is given in Figure
2.
IV. O UR PROPOSED METHOD
A. EddyNet architecture
The EddyNet architecture is based on the U-net architecture
[4]. It starts with an encoding (downsampling) path with 3
stages, where each stage consists of two 3 × 3 convolutional
layers followed by either a Scaled Exponential Linear Unit
(SELU) activation function [5] (referred to as EddyNet S)
or by the classical ReLU activation + Batch Normalization
(referred to as EddyNet), then a 2 × 2 max pooling layer that
halves the resolution of the input. The decoding (upsampling)
path uses transposed convolutions (also called deconvolutions)

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[30] to return to the original resolution. Like U-net, Eddynet
benefits from skip connections from the contracting path to
the expanding path to account for information originating
from early stages. Preliminary experiments with the original
architecture of U-Net showed a severe overfitting given the
low number of training samples compared to the capacity
of the architecture. Numerous attempts and hyperparameter
tuning led us to finally settle on a 3-stage all-32-filter architecture as shown in Figure 3. EddyNet has the benefit of
having a small number of parameters compared to widely
used architecture, thus resulting in low memory consumption.
Our neural network can still overfit the data which shows
that it can capture the nonlinear inverse problem of eddy
detection and classification. Hence, we add dropout layers
before each max pooling layer and before each transposed
convolutional layer; we chose these positions since they are
the ones involved in the concatenations where the highest
number of filters (64) is present. Dropout layers helped to
regularize the network and boosted the validation loss performance. Regarding EddyNet S, we mention three essential
considerations: i) The weight initialization is different than
with EddyNet, we detail this aspect in the experiment section.
ii) The theory behind the SELU activation function stands on
the self-normalizing property which aims to keep the inputs
close to a zero mean and unit variance through the network
layers. Classical dropout that randomly sets units to zero
could harm this property; [5] propose therefore a new dropout
technique called AlphaDropout that addresses this problem
by randomly setting activations on the negative saturation
value. iii) SELU theory is originally derived for Feed Forward
Networks, applying them to CNNs needs careful setting. In
preliminary experiments, using our U-net like architecture
with SELU activations resulted in a very noisy loss that even
explodes sometimes. We think this could be caused by the
skip connections that can violate the self-normalizing property
desired by the SELU, and hence decided to keep Batch
Normalization in EddyNet S after each of the maxpooling,
transposed convolution and concatenation layers.
B. Loss metric
While multiclass classification problems in deep learning
are generally trained using the categorical cross-entropy cost
function, segmentation problems favor the use of overlap based
metrics. The dice coefficient is a popular and largely used cost
function in segmentation problems. Considering the predicted
region P and the groundtruth region G, and by denoting |P |
and |G| the sum of elements in each area, the dice coefficient
is twice the ratio of the intersection over the sum of areas:
2|P ∩ G|
.
(1)
DiceCoef(P, G) =
|P | + |G|
A perfect segmentation result is given by a dice coefficient of
1, while a dice coefficient of 0 refers to a completely mistaken
segmentation. Seeing it from a F1-measure perspective, the
dice coefficient is the harmonic mean of the precision and
recall metrics.
The implementation uses one-hot encoding vectors, an
essential detail is that the loss function of EddyNet uses a

3

Fig. 3: EddyNet architecture

soft and differentiable version of the dice coefficient which
considers the output of the softmax layer as it is without
binarization:
P
2 i pi ∗ gi
P ,
softDiceCoef(P, G) = P
(2)
i pi +
i gi
where the pi are the probabilities given by the softmax layer
0 ≤ pi ≤ 1, and the gi are either 1 for the correct class and 0
either. We found later that a recent study used another version
of a soft dice loss [31]; a comparison of both versions is out
of the scope of this work.
Since we are in the context of a multiclass classification
problem, we try to maximize the performance of our network
using the mean of three one-vs-all soft dice coefficients of
each class. The loss function that our neural network aims to
minimize is then simply:
Dice Loss = 1 − softMeanDiceCoef

(3)

V. E XPERIMENTS
A. Assessment of the performance
Keras framework [32] with a Tensorflow backend is considered in this work. EddyNet is trained on a Nvidia K80
GPU card using ADAM optimizer [33] and mini-batches
of 16 maps. The weights were initialized using truncated
Gaussian distributed weights of zero mean and {2/number of
input units} variance [34] for EddyNet, while we use weights
drawn from a truncated Gaussian distribution of zero mean
and {1/number of input units} variance for EddyNet S. The
training dataset is split into 4080 images for training and 1020
for validation. We also use an early-stopping strategy to stop
the learning process when the validation dataset loss stops
improving in five consecutive epochs. EddyNet weights are
then the ones resulting in the lowest validation loss value.
EddyNet and EddyNet S are then compared regarding the
use of the classical ReLU+BN and the use of SELU. We also
compare the use of overlap based metric represented by the

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TABLE I: Metrics calculated from the results of 50 random sets of 360 SSH patches from the test dataset, we report the mean
value and put the standard variation between parenthesis.
Anticyclonic
EddyNet
EddyNet S

#Param
177,571

Epoch time
∼12 min
∼7 min

Train loss
Dice Loss
CCE
Dice Loss
CCE

0.708
0.695
0.694
0.682

(0.002)
(0.003)
(0.003)
(0.002)

Cyclonic
Dice Coef
0.677 (0.001)
0.651 (0.001)
0.665 (0.001)
0.653 (0.002)

Non Eddy
0.929
0.940
0.933
0.939

(0.001)
(0.001)
(0.001)
(0.001)

Mean
0.772
0.762
0.764
0.758

Dice Coef
(0.001)
(0.001)
(0.001)
(0.001)

Global Accuracy
88.60% (0.10%)
89.92% (0.07%)
88.98% (0.09%)
89.83% (0.08%)

B. Ghost eddies

(a)

The presence of ghost eddies is a frequent problem encountered in eddy detection and tracking algorithms [14]. Ghost
eddies are eddies that are found by the detection algorithm then
disappear between consecutive maps before reappearing again.
To point out the position of the missed ghost eddies, PET14
uses linear temporal interpolation between centers of detected
eddies and stores the positions of the centers of ghost eddies.
Using EddyNet we check if the pixels of ghost eddy centers
correspond to actual eddy detections. We found that EddyNet
assigns the centers of ghost eddies to the correct eddy classes
55% of the time for anticyclonic eddies, and 45% for cyclonic
eddies. EddyNet could be a relevant method to detect ghost
eddies that are missed out by conventional methods. Figure 5
illustrates two examples of ghost eddy detection.

VI. C ONCLUSION

(b)

Fig. 4: Examples of the eddy segmentation results using
Eddynet and EddyNet S: anticyclonic eddies (green), cyclonic
(brown), non eddy (blue)

Dice Loss (Equation 3), with the classical Categorical CrossEntropy (CCE). Table I compares the four combination in
terms of global accuracy and mean dice coefficient (original
not soft) averaged on 50 random sets of 360 SSH 120 × 120
maps from 2012. Training EddyNet S takes nearly half the
time needed for training EddyNet. Comparison regarding the
training loss function shows that training with the dice loss results in a higher dice coefficient for our two classes of interest
(cyclonic and anticyclonic) in both EddyNet and EddyNet S;
dice loss yields a better overall mean dice coefficient than
training with CCE loss. Regarding the effect of the activation
function, we obtained better metrics with EddyNet at the
cost of a longer training procedure. Visually Eddynet and
EddyNet S give close outputs as can be seen in Figure 4.

This work investigates the use of recent developments in
deep learning based image segmentation for an ocean remote
sensing problem, namely, eddy detection and classification
from Sea Surface Height (SSH) maps. We propose EddyNet,
a deep neural network architecture inspired from architectures
and ideas widely adopted in the computer vision community.
We transfer successfully the knowledge gained to the problem
of eddy classification by dealing with various challenges. Future work involves investigating the use of temporal volumes
of SSH and deriving a 3D version inspired by the works of
[31]. Adding other surface information such as Sea Surface
Temperature might also help improving the detection. Another
extension would be the application of EddyNet over the globe,
and assessing its general capacity over other regions. Postprocessing by constraining the eddies to verify additional
criteria and tracking the eddies was omitted in this work and
could also be developed in future work.
Beyond the illustrative aspect of this contribution, we offer
to the oceanic remote sensing community an easy and powerful tool that can save handcrafting model efforts. Any user can
employ his own eddy segmentation ”ground truth” and train
the model from scratch if he/she has the necessary memory
and computing resources, or simply use EddyNet provided
weights as an initialization then perform fine-tuning using
his/her dataset. One can also think of averaging results from
classical contour-based methods and EddyNet. In the spirit of
reproducibility, Python code is available at https://github.com/
redouanelg/eddynet, and we also share the training and testing
data used for this work to encourage competing methods and,
especially, other deep learning architectures.

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(a)

(b)

Fig. 5: Detection of ghost eddies: [left] SSH map where ghost
eddies centers are marked: anticyclonic (red dots), cyclonic
(blue dots). [center] PET14 segmentation. [right] EddyNet segmentation: anticyclonic (green), cyclonic (brown), non eddy
(blue)

ACKNOWLEDGMENT
The authors would like to thank Antoine Delepoulle,
Bertrand Chapron and Julien Le Sommer for their constructive comments. This work was supported by ANR (Agence
Nationale de la Recherche, grant ANR-13-MONU-0014) and
Labex Cominlabs (grant SEACS). Evan Mason is supported
by the Copernicus Marine Environment Monitoring Service
(CMEMS) MedSUB project.
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