preprints.org > environmental and earth sciences > oceanography > doi: 10.20944/preprints202312.0825.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 11 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (08:11:49 CET)

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

wind wave; X-band marine radar; significant wave height; Pierson-Moskowitz spectrum; synthetic radar images; preliminary training; artificial neural networks; machine learning; deep learning; convolutional neural networks; unsupervised pre-training

Environmental and Earth Sciences, Oceanography

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