Dense and Residual Neural Networks for Full-waveform LiDAR Echo Decomposition

Gangping Liu; Gangping Liu; Jun Ke; Jun Ke

doi:10.1364/ISA.2021.IF4D.3

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper IF4D.3
•https://doi.org/10.1364/ISA.2021.IF4D.3

Dense and Residual Neural Networks for Full-waveform LiDAR Echo Decomposition

Gangping Liu and Jun Ke

Imaging Systems and Applications 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Computational Imaging II (IF4D)

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G. Liu and J. Ke, "Dense and Residual Neural Networks for Full-waveform LiDAR Echo Decomposition," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper IF4D.3.

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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.

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