Dense and residual neural networks for full-waveform LiDAR echo decomposition

Gangping Liu; Jun Ke

Dense and residual neural networks for full-waveform LiDAR echo decomposition

Gangping Liu, Jun Ke^*

^*Corresponding author for this work

School of Optics and Photonics

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

Abstract

For full-waveform LiDAR echo signals, a high efficient and accurate decomposition method based on a dense (Full-waveform Dense Connection Network, FDCN) and a residual neural networks (Full-waveform Deep Residual Network, FDRN) is proposed in this paper.

Original language	English
Article number	IF4D.3
Journal	Optics InfoBase Conference Papers
Publication status	Published - 2021
Event	Imaging Systems and Applications, ISA 2021 - Part of OSA Imaging and Applied Optics Congress 2021 - Virtual, Online, United States Duration: 19 Jul 2021 → 23 Jul 2021

Cite this

@article{ab88ffcf64be4a67808475ae493547e4,

title = "Dense and residual neural networks for full-waveform LiDAR echo decomposition",

abstract = "For full-waveform LiDAR echo signals, a high efficient and accurate decomposition method based on a dense (Full-waveform Dense Connection Network, FDCN) and a residual neural networks (Full-waveform Deep Residual Network, FDRN) is proposed in this paper.",

author = "Gangping Liu and Jun Ke",

note = "Publisher Copyright: {\textcopyright}2021TheAuthor(s); Imaging Systems and Applications, ISA 2021 - Part of OSA Imaging and Applied Optics Congress 2021 ; Conference date: 19-07-2021 Through 23-07-2021",

year = "2021",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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TY - JOUR

T1 - Dense and residual neural networks for full-waveform LiDAR echo decomposition

AU - Liu, Gangping

AU - Ke, Jun

PY - 2021

Y1 - 2021

N2 - For full-waveform LiDAR echo signals, a high efficient and accurate decomposition method based on a dense (Full-waveform Dense Connection Network, FDCN) and a residual neural networks (Full-waveform Deep Residual Network, FDRN) is proposed in this paper.

AB - For full-waveform LiDAR echo signals, a high efficient and accurate decomposition method based on a dense (Full-waveform Dense Connection Network, FDCN) and a residual neural networks (Full-waveform Deep Residual Network, FDRN) is proposed in this paper.

UR - http://www.scopus.com/inward/record.url?scp=85119492187&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85119492187

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - IF4D.3

T2 - Imaging Systems and Applications, ISA 2021 - Part of OSA Imaging and Applied Optics Congress 2021

Y2 - 19 July 2021 through 23 July 2021

ER -

Dense and residual neural networks for full-waveform LiDAR echo decomposition

Abstract

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