Stochastic EM algorithm for fast analysis of single waveform multi-spectral Lidar data

Q. Legros, S. McLaughlin, Y. Altmann, S. Meignen

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

Abstract

This paper addresses the problem of estimating spectral and range profiles from single-photon Lidar waveforms associated with single surfaces in presence of an unknown background. A single Lidar waveform per pixel is considered, whereby a single detector is used to acquire information simultaneously at multiple wavelengths. A novel Bayesian approach is developed to perform the estimation of model parameters in a reduced computational time. This is achieved by transforming an EM-based algorithm recently proposed into a stochastic EM algorithm, which is computationally more attractive. The reconstruction performance and computational complexity of our approach are assessed through a series of experiments using synthetic data under different observation scenarios. The obtained results demonstrate a significant speed-up compared to the state-of-the-art method, without significant degradation of the estimation quality.

Original languageEnglish
Title of host publication2020 28th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages2413-2417
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 18 Dec 2020
Event28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
https://eusipco2020.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465

Conference

Conference28th European Signal Processing Conference
Abbreviated titleEUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period18/01/2122/01/21
Internet address

Keywords

  • 3D imaging
  • Bayesian estimation
  • Multispectral imaging
  • Single-photon Lidar

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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