Efficient Range Estimation and Material Quantification from Multispectral Lidar Waveforms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes a new Bayesian range estimation and spectral unmixing algorithm to analyse remote scenes sensed via multispectral Lidar measurements. To a first approximation, each Lidar waveform consists of the temporal signature of the observed target, which depends on the wavelength of the laser source considered and which is corrupted by Poisson noise. When the number of spectral bands considered is large enough, it becomes possible to identify and quantify the main materials in the scene, in addition to estimating classical Lidar-based range profiles. In this work, we adopt a Bayesian approach and the unknown model parameters are assigned prior distributions translating prior knowledge available (e.g., positivity, sparsity and/or smoothness). This prior model is then combined with the observation model (likelihood) to derive the joint posterior distribution of the unknown parameters which are inferred via maximum a posteriori estimation. Under mild assumptions often true in practice, we show that it is possible to find a global optimizer of the posterior by splitting the problem into two sequential steps estimating the unknown spectral quantities and the target ranges, respectively. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data aquired under controlled observation conditions.

Original languageEnglish
Title of host publication2016 Sensor Signal Processing for Defence (SSPD)
PublisherIEEE
ISBN (Electronic)9781509003266
DOIs
Publication statusPublished - 13 Oct 2016
Event6th Sensor Signal Processing for Defence Conference 2016 - Edinburgh, United Kingdom
Duration: 22 Sep 201623 Sep 2016
Conference number: 6th

Conference

Conference6th Sensor Signal Processing for Defence Conference 2016
Abbreviated titleSSPD 2016
CountryUnited Kingdom
CityEdinburgh
Period22/09/1623/09/16

Fingerprint

Optical radar
optical radar
waveforms
estimating
translating
spectral bands
signatures
methodology
Wavelength
Lasers
profiles
approximation
wavelengths
lasers
Experiments

Keywords

  • Depth imaging
  • Multispectral Lidar
  • Poisson noise
  • Spectral unmixing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics
  • Instrumentation
  • Artificial Intelligence

Cite this

@inproceedings{dbe3863e00584e9caa686dfbe651fb8c,
title = "Efficient Range Estimation and Material Quantification from Multispectral Lidar Waveforms",
abstract = "This paper describes a new Bayesian range estimation and spectral unmixing algorithm to analyse remote scenes sensed via multispectral Lidar measurements. To a first approximation, each Lidar waveform consists of the temporal signature of the observed target, which depends on the wavelength of the laser source considered and which is corrupted by Poisson noise. When the number of spectral bands considered is large enough, it becomes possible to identify and quantify the main materials in the scene, in addition to estimating classical Lidar-based range profiles. In this work, we adopt a Bayesian approach and the unknown model parameters are assigned prior distributions translating prior knowledge available (e.g., positivity, sparsity and/or smoothness). This prior model is then combined with the observation model (likelihood) to derive the joint posterior distribution of the unknown parameters which are inferred via maximum a posteriori estimation. Under mild assumptions often true in practice, we show that it is possible to find a global optimizer of the posterior by splitting the problem into two sequential steps estimating the unknown spectral quantities and the target ranges, respectively. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data aquired under controlled observation conditions.",
keywords = "Depth imaging, Multispectral Lidar, Poisson noise, Spectral unmixing",
author = "Yoann Altmann and Aurora Maccarone and Abderrahim Halimi and Aongus McCarthy and Gerald Buller and Steve McLaughlin",
year = "2016",
month = "10",
day = "13",
doi = "10.1109/SSPD.2016.7590596",
language = "English",
booktitle = "2016 Sensor Signal Processing for Defence (SSPD)",
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address = "United States",

}

Altmann, Y, Maccarone, A, Halimi, A, McCarthy, A, Buller, G & McLaughlin, S 2016, Efficient Range Estimation and Material Quantification from Multispectral Lidar Waveforms. in 2016 Sensor Signal Processing for Defence (SSPD)., 7590596, IEEE, 6th Sensor Signal Processing for Defence Conference 2016, Edinburgh, United Kingdom, 22/09/16. https://doi.org/10.1109/SSPD.2016.7590596

Efficient Range Estimation and Material Quantification from Multispectral Lidar Waveforms. / Altmann, Yoann; Maccarone, Aurora; Halimi, Abderrahim; McCarthy, Aongus; Buller, Gerald; McLaughlin, Steve.

2016 Sensor Signal Processing for Defence (SSPD). IEEE, 2016. 7590596.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Efficient Range Estimation and Material Quantification from Multispectral Lidar Waveforms

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AU - McLaughlin, Steve

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AB - This paper describes a new Bayesian range estimation and spectral unmixing algorithm to analyse remote scenes sensed via multispectral Lidar measurements. To a first approximation, each Lidar waveform consists of the temporal signature of the observed target, which depends on the wavelength of the laser source considered and which is corrupted by Poisson noise. When the number of spectral bands considered is large enough, it becomes possible to identify and quantify the main materials in the scene, in addition to estimating classical Lidar-based range profiles. In this work, we adopt a Bayesian approach and the unknown model parameters are assigned prior distributions translating prior knowledge available (e.g., positivity, sparsity and/or smoothness). This prior model is then combined with the observation model (likelihood) to derive the joint posterior distribution of the unknown parameters which are inferred via maximum a posteriori estimation. Under mild assumptions often true in practice, we show that it is possible to find a global optimizer of the posterior by splitting the problem into two sequential steps estimating the unknown spectral quantities and the target ranges, respectively. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data aquired under controlled observation conditions.

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