International Forum for Green Asia 2020

Deep Learning-Based Estimation of Whitecap Coverage on the Ocean Surface
Yukun WANG1,2, Yuji SUGIHARA3 and Osama ELJAMAL3
1

Interdisciplinary Graduate School of Engineering, Kyushu University
2
Green Asia Education Center, Kyushu University
3
Faculty of Engineering Sciences, Kyushu University
Kasuga-koen, Kasuga, Fukuoka 816-8580, Japan,

Abstract
Whitecaps generated by wave breaking on the ocean surface play an important role in the local
interaction across the air-sea interface. In this study, a progressive high-precision whitecap
extraction model is first built by using the algorithm of deep learning. Compared with a
traditional whitecap extraction model based on threshold value, the algorithm is found to solve
problems caused by illumination condition and color change on the ocean surface, andeffectively extracts fine whitecaps with complicated structures. Further, through comparisonswith algorithms such as Automatic Whitecap Extraction (AWE), Iterative Between Class
Variance (IBCV) and the whitecap extraction based on fixed threshold value, the present
algorithm is demonstrated to be more effective and accurate for identifying whitecaps, and it
reduces the amount of evaluation load, and can effectively apply for changeable oceanconditions.
1. Introduction
Whitecaps have a significant effect on
the exchange of momentum, heat and CO2
between the atmosphere and the ocean
surface. Figure 1 shows the whitecaps
image.
Whitecap coverage is defined as
the area of whitecap area per unit area of sea
area. Whitecap coverage is considered to
be a useful wave breaking property forquantifying various sea surface fluxes and its
evaluation is important for describing theexchange process between the atmosphereand the ocean.
In this study, a deep learning algorithm
is applied to extract whitecap areas from sea
surface images, in order to construct a highprecision whitecap extraction model. For
solving the problem of the reflected light on
the sea surface and the change in the sea color,a new Semantic Whitecap Extraction model(SWE) is made to solving problems in theprevious whitecap wave extraction models
based on the image brightness threshold.
The new whitecap extraction technology isused to determine whitecap coverage whenshooting digital images under complicatedsea surface conditions.

Fig. 1 Image of whitecaps on the ocean

2. Whitecaps Extraction Model
The definition of whitecap coverage isthe ratio of the number of pixels that areconsidered to be whitecap to the number ofall pixels in the image.
!! (%) = (&!⁄&') × 100

where NW is the number of pixels in thewhitecap and NA is the number of pixels in theimage.
Various analysis methods havebeen proposed in previous studies. The FT
method, which is the most representative
method, is a method to classify whitecap
regions and background regions using fixed9International Forum for Green Asia 2020
Recognition ofcomplex contours
and cavities

the threshold value to achieve the highest
class separation assuming that there are twoclasses in the image: a bright image part anda dark part.
Furthermore, the AWE
method is an algorithm that automaticallydetermines the optimal threshold based onderivative analysis.
However, it isdifficult to obtain ideal results with thesemethods because they cannot respond tochanges in sea color and reflected light.
In this study, a new extraction model
called SWE (Semantic Whitecap Extraction
Model)was designed by analyzing 400
labeled images to address the negative effects
of sea color and reflected light, using seasurface images under various sunlight
conditions and at different times of day.
As Fig. 2 shown, SWE model is trained onimages of various conditions, so it is able torespond to changes in sunlight conditions andsea surface color, which eliminates thenegative effects of these conditions andachieves high-precision whitecaps extraction.
In addition, the developed deep learning
method of whitecaps extraction uses
Semantic's method, which uses not only theinformation of each pixel but also theinformation of surrounding pixels to detect
the whitecaps.

Eliminating sun lighteffects

Recognition of micro Eliminating sea surface
breaking waves
color reffects
Fig. 2 Features of the SWE modelFig. 3 Relation between Wc_SWE with

3. Results and Discussion
By comparing with Wc_MANUAL,
Wc_SWE with high accuracy can be known
based on Fig. 3. Relationship between
whitecap coverage and wind speed at 10 m
above sea level as shown in Fig. 4, the resultsof whitecap coverage calculated by the FTand SWE methods, respectively, from theimages obtained in this study. From thefigure, Wc_SWE is in good agreement withthe traditional empirical relationship when
the wind speed is above 10 m/s. The valuesof Wc_SWE are relatively high, but showhigh consistency with the relationship of
Wc_FT230, which has a very high thresholdof 230 for calculating the whitewave
coverage. Since the values are low becauseof the setting, the calculation results of theSWE method should be higher than those of
the FT230 method.

Wc_MANUAL

Fig. 4 Relation between WC with U10threshold values, which are determined byanalyzing the image. The IBCV method is abinarized filter that automatically determines
10