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Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images

Accurate water surface elevation estimation is essential for understanding nearshore processes, but it is still challenging due to limitations in measuring water level using in-situ acoustic sensors. This paper presents a vision-based water surface elevation estimation approach using multi-view data...

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Autores principales: Kim, Jinah, Kim, Taekyung, Oh, Sang-Ho, Do, Kideok, Ryu, Joon-Gyu, Kim, Jaeil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571332/
https://www.ncbi.nlm.nih.gov/pubmed/34741087
http://dx.doi.org/10.1038/s41598-021-01157-x
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author Kim, Jinah
Kim, Taekyung
Oh, Sang-Ho
Do, Kideok
Ryu, Joon-Gyu
Kim, Jaeil
author_facet Kim, Jinah
Kim, Taekyung
Oh, Sang-Ho
Do, Kideok
Ryu, Joon-Gyu
Kim, Jaeil
author_sort Kim, Jinah
collection PubMed
description Accurate water surface elevation estimation is essential for understanding nearshore processes, but it is still challenging due to limitations in measuring water level using in-situ acoustic sensors. This paper presents a vision-based water surface elevation estimation approach using multi-view datasets. Specifically, we propose a visual domain adaptation method to build a water level estimator in spite of a situation in which ocean wave height cannot be measured directly. We also implemented a semi-supervised approach to extract wave height information from long-term sequences of wave height observations with minimal supervision. We performed wave flume experiments in a hydraulic laboratory with two cameras with side and top viewpoints to validate the effectiveness of our approach. The performance of the proposed models were evaluated by comparing the estimated time series of water elevation with the ground-truth wave gauge data at three locations along the wave flume. The estimated time series were in good agreement within the averaged correlation coefficient of 0.98 and 0.90 on the measurement and 0.95 and 0.85 on the estimation for regular and irregular waves, respectively.
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spelling pubmed-85713322021-11-09 Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images Kim, Jinah Kim, Taekyung Oh, Sang-Ho Do, Kideok Ryu, Joon-Gyu Kim, Jaeil Sci Rep Article Accurate water surface elevation estimation is essential for understanding nearshore processes, but it is still challenging due to limitations in measuring water level using in-situ acoustic sensors. This paper presents a vision-based water surface elevation estimation approach using multi-view datasets. Specifically, we propose a visual domain adaptation method to build a water level estimator in spite of a situation in which ocean wave height cannot be measured directly. We also implemented a semi-supervised approach to extract wave height information from long-term sequences of wave height observations with minimal supervision. We performed wave flume experiments in a hydraulic laboratory with two cameras with side and top viewpoints to validate the effectiveness of our approach. The performance of the proposed models were evaluated by comparing the estimated time series of water elevation with the ground-truth wave gauge data at three locations along the wave flume. The estimated time series were in good agreement within the averaged correlation coefficient of 0.98 and 0.90 on the measurement and 0.95 and 0.85 on the estimation for regular and irregular waves, respectively. Nature Publishing Group UK 2021-11-05 /pmc/articles/PMC8571332/ /pubmed/34741087 http://dx.doi.org/10.1038/s41598-021-01157-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kim, Jinah
Kim, Taekyung
Oh, Sang-Ho
Do, Kideok
Ryu, Joon-Gyu
Kim, Jaeil
Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
title Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
title_full Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
title_fullStr Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
title_full_unstemmed Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
title_short Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
title_sort deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571332/
https://www.ncbi.nlm.nih.gov/pubmed/34741087
http://dx.doi.org/10.1038/s41598-021-01157-x
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