Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network

In this article, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height (<inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula>) from X-band nautical radar images. Three types of features are first ex...

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Autores principales:	Weimin Huang, Zhiding Yang, Xinwei Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Significant wave height (<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$H_s$</tex-math> </inline-formula>) temporal convolutional network (TCN) X-band nautical radar Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/fab7438fd7534d46837baaaa5507f28a
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spelling	oai:doaj.org-article:fab7438fd7534d46837baaaa5507f28a2021-11-20T00:00:22ZWave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network2151-153510.1109/JSTARS.2021.3124969https://doaj.org/article/fab7438fd7534d46837baaaa5507f28a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9601171/https://doaj.org/toc/2151-1535In this article, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height (<inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula>) from X-band nautical radar images. Three types of features are first extracted from radar image sequences based on signal-to-noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and gray level cooccurrence matrix methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce <inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula> estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as the simultaneous wave data measured by several wave buoys deployed nearby are used for model training and testing. Experimental results after averaging show that TCN-based model further improves the <inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula> estimation accuracy, with reductions of root-mean-square errors by 0.33 and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that under the same feature extraction scheme, TCN outperforms other machine learning-based algorithms including support vector regression and the gated recurrent unit network.Weimin HuangZhiding YangXinwei ChenIEEEarticleSignificant wave height (<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$H_s$</tex-math> </inline-formula>)temporal convolutional network (TCN)X-band nautical radarOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11395-11405 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Significant wave height (<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$H_s$</tex-math> </inline-formula>) temporal convolutional network (TCN) X-band nautical radar Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Significant wave height (<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$H_s$</tex-math> </inline-formula>) temporal convolutional network (TCN) X-band nautical radar Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Weimin Huang Zhiding Yang Xinwei Chen Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
description	In this article, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height (<inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula>) from X-band nautical radar images. Three types of features are first extracted from radar image sequences based on signal-to-noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and gray level cooccurrence matrix methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce <inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula> estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as the simultaneous wave data measured by several wave buoys deployed nearby are used for model training and testing. Experimental results after averaging show that TCN-based model further improves the <inline-formula><tex-math notation="LaTeX">$H_s$</tex-math></inline-formula> estimation accuracy, with reductions of root-mean-square errors by 0.33 and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that under the same feature extraction scheme, TCN outperforms other machine learning-based algorithms including support vector regression and the gated recurrent unit network.
format	article
author	Weimin Huang Zhiding Yang Xinwei Chen
author_facet	Weimin Huang Zhiding Yang Xinwei Chen
author_sort	Weimin Huang
title	Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
title_short	Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
title_full	Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
title_fullStr	Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
title_full_unstemmed	Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
title_sort	wave height estimation from x-band nautical radar images using temporal convolutional network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fab7438fd7534d46837baaaa5507f28a
work_keys_str_mv	AT weiminhuang waveheightestimationfromxbandnauticalradarimagesusingtemporalconvolutionalnetwork AT zhidingyang waveheightestimationfromxbandnauticalradarimagesusingtemporalconvolutionalnetwork AT xinweichen waveheightestimationfromxbandnauticalradarimagesusingtemporalconvolutionalnetwork
_version_	1718419874283257856

Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network

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