Fast Classification and Depth Estimation for Multispectral Single-Photon LiDAR Data

Mohamed Amir Alaa Belmekki, Stephen McLaughlin, Abderrahim Halimi

Research output: Contribution to conferencePaperpeer-review

Abstract

Multispectral 3D LiDAR imaging plays an important role in the remote sensing community as it can provide rich spectral and depth information from targets. This paper proposes a fast pixel-wise classification algorithm for multispectral single-photon LiDAR imaging. The algorithm allows the detection of histograms containing surfaces with specific spectral signatures (i.e., specific materials) and discarding those histograms without reflective surfaces. The proposed Bayesian model is carefully built to allow the marginalization of latent variables leading to a tractable formulation and fast estimation of the parameters of interest, together with their uncertainties. Results on simulated and real single-photon data illustrates the robustness and good performance of this approach.
Original languageEnglish
Publication statusAccepted/In press - 2 Jul 2021
Event10th International Conference in Sensor Signal Processing for Defence: from Sensor to Decision - Edinburgh, United Kingdom
Duration: 14 Sep 202115 Sep 2021

Conference

Conference10th International Conference in Sensor Signal Processing for Defence
Abbreviated titleSSPD2021
CountryUnited Kingdom
CityEdinburgh
Period14/09/2115/09/21

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