Predicting sea surface wave and wind parameters from satellite radar images using machine learning
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2023
Författare
Borg, Filip
Brobeck, Axel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurate predictions of wave and wind parameters over oceans are crucial for various
marine operations. Although buoys provide accurate measurements, their deployment
is limited, which necessitates the exploration of alternative data sources. Sentinel-1,
a satellite mission capturing Synthetic Aperture Radar (SAR) images with high
coverage, presents a promising opportunity. However, establishing the relationship
between SAR images and wave/wind parameters is not straightforward. This project
aims to develop a machine learning model that can effectively extract this relationship.
To accomplish this, data from all available buoys measuring significant wave height
and wind speed in the year 2021 were utilized. The corresponding SAR images were
located, and 2 km×2 km sub-images were extracted around each buoy. From each
sub-image, a set of features were extracted. These sub-images and features served as
input to train machine learning models capable of predicting buoy measurements,
supplemented with model data as necessary.
The project presents two final deep learning models: one utilizing only the extracted
features and another employing both the sub-images and features. These multi-class
regression models simultaneously predict significant wave height and wind speed. The
model using only features achieved a Root Mean Square Error (RMSE) of 0.553 m for
significant wave height and 1.573 m/s for wind speed. The model incorporating both
sub-images and features achieved an RMSE of 0.459 m for significant wave height
and 1.658 m/s for wind speed.
The code for the project can be found on https://github.com/SEE-GEO/sarssw.
Beskrivning
Ämne/nyckelord
Machine Learning, Computer Vision, Synthetic Aperture Radar, Sig nificant Wave Height, Wind Speed, Radar, Master Thesis, Chalmers University of Technology