Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators

Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR (airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3-D point clouds but also a deep learning method for points and their waveforms observed by full-waveform LiDA...

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Autores principales:	Takayuki Shinohara, Haoyi Xiu, Masashi Matsuoka
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Airborne LiDAR conditional generative adversarial network deep learning full waveform LiDAR Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/96f93527a30b4b37b508e9bbb88b7271
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id	oai:doaj.org-article:96f93527a30b4b37b508e9bbb88b7271
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spelling	oai:doaj.org-article:96f93527a30b4b37b508e9bbb88b72712021-11-25T00:00:13ZPoint2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators2151-153510.1109/JSTARS.2021.3124610https://doaj.org/article/96f93527a30b4b37b508e9bbb88b72712021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599498/https://doaj.org/toc/2151-1535Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR (airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3-D point clouds but also a deep learning method for points and their waveforms observed by full-waveform LiDAR (airborne FW data) was proposed. We need to achieve highly accurate land cover classification by using airborne FW data, but open data often only have airborne 3-D point clouds available. Therefore, to improve the performance of land cover classification when using airborne 3-D point clouds published as open data, it is important to restore waveforms from airborne 3-D point clouds. In this article, we propose a deep learning model to translate an airborne 3-D point cloud to airborne FW data (called a point-to-waveform translation model, point2wave) using a conditional generative adversarial net (cGAN). Our point2wave is a cGAN pipeline consisting of a generator that translates the waveform corresponding to each point from the input airborne 3-D point cloud and discriminators that calculate the distance between the translated waveform and the ground truth waveform. Using a set of point clouds and waveforms dataset, we have experimented to translate points into the waveforms by point2wave. Experimental results showed that point2wave could translate waveforms from the airborne 3-D point cloud and the translated fake waveforms achieved nearly the same land cover classification performance as the real waveforms.Takayuki ShinoharaHaoyi XiuMasashi MatsuokaIEEEarticleAirborne LiDARconditional generative adversarial networkdeep learningfull waveform LiDAROcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11630-11642 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Airborne LiDAR conditional generative adversarial network deep learning full waveform LiDAR Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Airborne LiDAR conditional generative adversarial network deep learning full waveform LiDAR Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Takayuki Shinohara Haoyi Xiu Masashi Matsuoka Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
description	Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR (airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3-D point clouds but also a deep learning method for points and their waveforms observed by full-waveform LiDAR (airborne FW data) was proposed. We need to achieve highly accurate land cover classification by using airborne FW data, but open data often only have airborne 3-D point clouds available. Therefore, to improve the performance of land cover classification when using airborne 3-D point clouds published as open data, it is important to restore waveforms from airborne 3-D point clouds. In this article, we propose a deep learning model to translate an airborne 3-D point cloud to airborne FW data (called a point-to-waveform translation model, point2wave) using a conditional generative adversarial net (cGAN). Our point2wave is a cGAN pipeline consisting of a generator that translates the waveform corresponding to each point from the input airborne 3-D point cloud and discriminators that calculate the distance between the translated waveform and the ground truth waveform. Using a set of point clouds and waveforms dataset, we have experimented to translate points into the waveforms by point2wave. Experimental results showed that point2wave could translate waveforms from the airborne 3-D point cloud and the translated fake waveforms achieved nearly the same land cover classification performance as the real waveforms.
format	article
author	Takayuki Shinohara Haoyi Xiu Masashi Matsuoka
author_facet	Takayuki Shinohara Haoyi Xiu Masashi Matsuoka
author_sort	Takayuki Shinohara
title	Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
title_short	Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
title_full	Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
title_fullStr	Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
title_full_unstemmed	Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
title_sort	point2wave: 3-d point cloud to waveform translation using a conditional generative adversarial network with dual discriminators
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/96f93527a30b4b37b508e9bbb88b7271
work_keys_str_mv	AT takayukishinohara point2wave3dpointcloudtowaveformtranslationusingaconditionalgenerativeadversarialnetworkwithdualdiscriminators AT haoyixiu point2wave3dpointcloudtowaveformtranslationusingaconditionalgenerativeadversarialnetworkwithdualdiscriminators AT masashimatsuoka point2wave3dpointcloudtowaveformtranslationusingaconditionalgenerativeadversarialnetworkwithdualdiscriminators
_version_	1718414708462059520

Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators

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