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

Abstract 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-...

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Autores principales:	Jinah Kim, Taekyung Kim, Sang-Ho Oh, Kideok Do, Joon-Gyu Ryu, Jaeil Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aa8a66427a8b4846aa6eb06241caec62
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id	oai:doaj.org-article:aa8a66427a8b4846aa6eb06241caec62
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spelling	oai:doaj.org-article:aa8a66427a8b4846aa6eb06241caec622021-11-08T10:55:15ZDeep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images10.1038/s41598-021-01157-x2045-2322https://doaj.org/article/aa8a66427a8b4846aa6eb06241caec622021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01157-xhttps://doaj.org/toc/2045-2322Abstract 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.Jinah KimTaekyung KimSang-Ho OhKideok DoJoon-Gyu RyuJaeil KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinah Kim Taekyung Kim Sang-Ho Oh Kideok Do Joon-Gyu Ryu Jaeil Kim Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
description	Abstract 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.
format	article
author	Jinah Kim Taekyung Kim Sang-Ho Oh Kideok Do Joon-Gyu Ryu Jaeil Kim
author_facet	Jinah Kim Taekyung Kim Sang-Ho Oh Kideok Do Joon-Gyu Ryu Jaeil Kim
author_sort	Jinah Kim
title	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_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_sort	deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aa8a66427a8b4846aa6eb06241caec62
work_keys_str_mv	AT jinahkim deepvisualdomainadaptationandsemisupervisedsegmentationforunderstandingwaveelevationusingwaveflumevideoimages AT taekyungkim deepvisualdomainadaptationandsemisupervisedsegmentationforunderstandingwaveelevationusingwaveflumevideoimages AT sanghooh deepvisualdomainadaptationandsemisupervisedsegmentationforunderstandingwaveelevationusingwaveflumevideoimages AT kideokdo deepvisualdomainadaptationandsemisupervisedsegmentationforunderstandingwaveelevationusingwaveflumevideoimages AT joongyuryu deepvisualdomainadaptationandsemisupervisedsegmentationforunderstandingwaveelevationusingwaveflumevideoimages AT jaeilkim deepvisualdomainadaptationandsemisupervisedsegmentationforunderstandingwaveelevationusingwaveflumevideoimages
_version_	1718442555967799296

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

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