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

^*Corresponding author for this work

School of Optics and Photonics

Beijing Institute of Technology
Ministry of Education in China
The University of Hong Kong

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	JW2A.29
Journal	Optics InfoBase Conference Papers
Publication status	Published - 2020
Event	Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 - Virtual, Online, United States Duration: 22 Jun 2020 → 26 Jun 2020

Cite this

@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 - https://www.scopus.com/pages/publications/85118605788

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

Abstract

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