CNN-based Super-resolution Full-waveform LiDAR

Gangping Liu; Jun Ke; Edmund Y. Lam

CNN-based Super-resolution Full-waveform LiDAR

Gangping Liu, Jun Ke^*, Edmund Y. Lam

^*此作品的通讯作者

光电学院

科研成果: 期刊稿件 › 会议文章 › 同行评审

摘要

Instead of using multiple sets of measurements, we discuss a CNN with one set of data to obtain temporal super-resolution in full-waveform LiDAR. The super-resolution results can enhance further waveform decomposition or classification performance.

源语言	英语
文章编号	JW2A.29
期刊	Optics InfoBase Conference Papers
出版状态	已出版 - 2020
活动	Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 - Virtual, Online, 美国期限: 22 6月 2020 → 26 6月 2020

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@article{7edb42789471460e952f961aadedd9b6,

title = "CNN-based Super-resolution Full-waveform LiDAR",

abstract = "Instead of using multiple sets of measurements, we discuss a CNN with one set of data to obtain temporal super-resolution in full-waveform LiDAR. The super-resolution results can enhance further waveform decomposition or classification performance.",

author = "Gangping Liu and Jun Ke and Lam, {Edmund Y.}",

note = "Publisher Copyright: {\textcopyright} 2020 OSA - The Optical Society. All rights reserved.; Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 ; Conference date: 22-06-2020 Through 26-06-2020",

year = "2020",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - CNN-based Super-resolution Full-waveform LiDAR

AU - Liu, Gangping

AU - Ke, Jun

AU - Lam, Edmund Y.

PY - 2020

Y1 - 2020

N2 - Instead of using multiple sets of measurements, we discuss a CNN with one set of data to obtain temporal super-resolution in full-waveform LiDAR. The super-resolution results can enhance further waveform decomposition or classification performance.

AB - Instead of using multiple sets of measurements, we discuss a CNN with one set of data to obtain temporal super-resolution in full-waveform LiDAR. The super-resolution results can enhance further waveform decomposition or classification performance.

UR - http://www.scopus.com/inward/record.url?scp=85118605788&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85118605788

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - JW2A.29

T2 - Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

Y2 - 22 June 2020 through 26 June 2020

ER -

CNN-based Super-resolution Full-waveform LiDAR

摘要

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引用此