The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 |
doi: 10.3389/fmars.2024.1363135
This article is part of the Research Topic Interpretable Machine Learning in Remote Sensing: Image Analysis and Classification View all articles
Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery
Provisionally accepted- 1 Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- 2 P.P. Shirshov Institute of Oceanology (RAS), Moscow, Moscow Oblast, Russia
X-band nautical radar captures the signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information about wind wave parameters. Traditional methods of significant wave height (SWH) estimation rely on 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 ANNbased approaches necessitate costly in situ data. In this study, as a viable alternative, we propose generating synthetic radar images with specified wave parameters using Fourier-based approach and Pierson-Moskowitz wave spectrum. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of the reconstruction ANN.After that, we train the regression ANN based on the previous convolutional part to obtain SWH back from the synthetic images. Then, we apply preliminary trained weights for the regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in SWH estimation accuracy from radar images with preliminary training on synthetic data.
Keywords: wind waves, X-band marine radar, significant wave height, synthetic radar images, machine learning, deep learning, Convolutional Neural Networks, unsupervised preliminary training
Received: 29 Dec 2023; Accepted: 02 Sep 2024.
Copyright: © 2024 Rezvov, Krinitskiy, Gavrikov, Golikov, Borisov, Suslov and Tilinina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Vadim Rezvov, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.