Joint Reconstruction of Multitemporal or Multispectral Single-Photon 3D LiDAR Images

Abderrahim Halimi; Rachael Tobin; Aongus McCarthy; Jose M. Bioucas-Dias; Stephen McLaughlin; Gerald Stuart Buller

doi:10.1109/SSPD.2019.8751664

Joint Reconstruction of Multitemporal or Multispectral Single-Photon 3D LiDAR Images

Abderrahim Halimi, Rachael Tobin, Aongus McCarthy, Jose M. Bioucas-Dias, Stephen McLaughlin, Gerald Stuart Buller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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Abstract

The aim of this paper is to propose a specialized algorithm to process Multitemporal or Multispectral 3D single-photon Lidar images. Of particular interest are challenging scenarios often encountered in real world, i.e., imaging through obscurants such as water, fog or imaging multilayered targets such as target behind camouflage. To restore the data, the algorithm accounts for data Poisson statistics and available prior knowledge regarding target depth and reflectivity estimates. More precisely, it accounts for (a) the non-local spatial correlations between pixels, (b) the spatial clustering of target returned photons and (c) spectral and temporal correlations between frames. An alternating direction method of multipliers (ADMM) algorithm is used to minimize the resulting cost function since it offers good convergence properties. The algorithm is validated on real data which show the benefit of the proposed strategy especially when dealing with multi-dimensional 3D data.

Original language	English
Title of host publication	2019 Sensor Signal Processing for Defence Conference (SSPD)
Publisher	IEEE
ISBN (Electronic)	9781728105031
DOIs	https://doi.org/10.1109/SSPD.2019.8751664
Publication status	Published - 1 Jul 2019
Event	8th Sensor Signal Processing for Defence Conference 2019 - Brighton, United Kingdom Duration: 9 May 2019 → 10 May 2019

Conference

Conference	8th Sensor Signal Processing for Defence Conference 2019
Abbreviated title	SSPD 2019
Country/Territory	United Kingdom
City	Brighton
Period	9/05/19 → 10/05/19

Keywords

3D Lidar imaging
Admm
Collaborative sparsity
Multispectral/multitemporal
Non-local total variation
Nr3d
Poisson statistics

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing
Aerospace Engineering
Control and Optimization

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@inproceedings{5da288f29dc9439eaf6521aac79945e1,

title = "Joint Reconstruction of Multitemporal or Multispectral Single-Photon 3D LiDAR Images",

abstract = "The aim of this paper is to propose a specialized algorithm to process Multitemporal or Multispectral 3D single-photon Lidar images. Of particular interest are challenging scenarios often encountered in real world, i.e., imaging through obscurants such as water, fog or imaging multilayered targets such as target behind camouflage. To restore the data, the algorithm accounts for data Poisson statistics and available prior knowledge regarding target depth and reflectivity estimates. More precisely, it accounts for (a) the non-local spatial correlations between pixels, (b) the spatial clustering of target returned photons and (c) spectral and temporal correlations between frames. An alternating direction method of multipliers (ADMM) algorithm is used to minimize the resulting cost function since it offers good convergence properties. The algorithm is validated on real data which show the benefit of the proposed strategy especially when dealing with multi-dimensional 3D data.",

keywords = "3D Lidar imaging, Admm, Collaborative sparsity, Multispectral/multitemporal, Non-local total variation, Nr3d, Poisson statistics",

author = "Abderrahim Halimi and Rachael Tobin and Aongus McCarthy and Bioucas-Dias, {Jose M.} and Stephen McLaughlin and Buller, {Gerald Stuart}",

year = "2019",

month = jul,

day = "1",

doi = "10.1109/SSPD.2019.8751664",

language = "English",

booktitle = "2019 Sensor Signal Processing for Defence Conference (SSPD)",

publisher = "IEEE",

address = "United States",

note = "8th Sensor Signal Processing for Defence Conference 2019, SSPD 2019 ; Conference date: 09-05-2019 Through 10-05-2019",

}

Halimi, A, Tobin, R, McCarthy, A, Bioucas-Dias, JM, McLaughlin, S & Buller, GS 2019, Joint Reconstruction of Multitemporal or Multispectral Single-Photon 3D LiDAR Images. in 2019 Sensor Signal Processing for Defence Conference (SSPD)., 8751664, IEEE, 8th Sensor Signal Processing for Defence Conference 2019, Brighton, United Kingdom, 9/05/19. https://doi.org/10.1109/SSPD.2019.8751664

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AU - Halimi, Abderrahim

AU - Tobin, Rachael

AU - McCarthy, Aongus

AU - Bioucas-Dias, Jose M.

AU - McLaughlin, Stephen

AU - Buller, Gerald Stuart

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The aim of this paper is to propose a specialized algorithm to process Multitemporal or Multispectral 3D single-photon Lidar images. Of particular interest are challenging scenarios often encountered in real world, i.e., imaging through obscurants such as water, fog or imaging multilayered targets such as target behind camouflage. To restore the data, the algorithm accounts for data Poisson statistics and available prior knowledge regarding target depth and reflectivity estimates. More precisely, it accounts for (a) the non-local spatial correlations between pixels, (b) the spatial clustering of target returned photons and (c) spectral and temporal correlations between frames. An alternating direction method of multipliers (ADMM) algorithm is used to minimize the resulting cost function since it offers good convergence properties. The algorithm is validated on real data which show the benefit of the proposed strategy especially when dealing with multi-dimensional 3D data.

AB - The aim of this paper is to propose a specialized algorithm to process Multitemporal or Multispectral 3D single-photon Lidar images. Of particular interest are challenging scenarios often encountered in real world, i.e., imaging through obscurants such as water, fog or imaging multilayered targets such as target behind camouflage. To restore the data, the algorithm accounts for data Poisson statistics and available prior knowledge regarding target depth and reflectivity estimates. More precisely, it accounts for (a) the non-local spatial correlations between pixels, (b) the spatial clustering of target returned photons and (c) spectral and temporal correlations between frames. An alternating direction method of multipliers (ADMM) algorithm is used to minimize the resulting cost function since it offers good convergence properties. The algorithm is validated on real data which show the benefit of the proposed strategy especially when dealing with multi-dimensional 3D data.

KW - 3D Lidar imaging

KW - Admm

KW - Collaborative sparsity

KW - Multispectral/multitemporal

KW - Non-local total variation

KW - Nr3d

KW - Poisson statistics

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U2 - 10.1109/SSPD.2019.8751664

DO - 10.1109/SSPD.2019.8751664

M3 - Conference contribution

BT - 2019 Sensor Signal Processing for Defence Conference (SSPD)

PB - IEEE

T2 - 8th Sensor Signal Processing for Defence Conference 2019

Y2 - 9 May 2019 through 10 May 2019

ER -

Joint Reconstruction of Multitemporal or Multispectral Single-Photon 3D LiDAR Images

Abstract

Conference

Keywords

ASJC Scopus subject areas

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