Fast Task-Based Adaptive Sampling For 3D Single-Photon Multispectral LiDAR Data

Mohamed Amir Alaa Belmekki, Rachael Tobin, Gerald Stuart Buller, Stephen McLaughlin, Abderrahim Halimi

Research output: Contribution to journalArticlepeer-review

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Abstract

3D single-photon LiDAR imaging plays an important role in numerous applications. However, long acquisition times and significant data volumes present a challenge for LiDAR imaging. This paper proposes a task-optimized adaptive sampling framework that enables fast acquisition and processing of high-dimensional single-photon LiDAR data. Given a task of interest, the iterative sampling strategy targets the most informative regions of a scene which are defined as those minimizing parameter uncertainties. The task is performed by considering a Bayesian model that is carefully built to allow fast per-pixel computations while delivering parameter estimates with quantified uncertainties. The framework is demonstrated on multispectral 3D single-photon LiDAR imaging when considering object classification and/or target detection as tasks. It is also analysed for both sequential and parallel scanning modes for different detector array sizes. Results on simulated and real data show the benefit of the proposed optimized sampling strategy when compared to state-of-the-art sampling strategies.
Original languageEnglish
Pages (from-to)174-187
Number of pages14
JournalIEEE Transactions on Computational Imaging
Volume8
Early online date14 Feb 2022
DOIs
Publication statusPublished - 2022

Keywords

  • 3D multispectral imaging
  • Adaptive sampling
  • Bayesian estimation
  • Estimation
  • Imaging
  • Laser radar
  • Object detection
  • Photonics
  • Poisson statistics
  • Task analysis
  • Uncertainty
  • classification
  • robust estimation
  • single-photon LiDAR
  • target detection

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computational Mathematics

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