Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms

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

Abstract

This paper presents a new Bayesian clustering method to analyse remotescenes sensed via multispectral Lidar measurements. To a firstapproximation, each Lidar waveform mainly consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. By sensing the scene at several wavelengths, we expect amore accurate target range estimation and a more efficient spectralanalysis of the scene. Thanks to its spectral classification capability,the proposed hierarchical Bayesian model, coupled with an efficientMarkov chain Monte Carlo algorithm, allows the estimation of depthimages together with reflectivity-based scene segmentation images.The proposed methodology is illustrated via experiments conductedwith real multispectral Lidar data.
Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages513-517
Number of pages5
ISBN (Electronic)9780992862657
DOIs
Publication statusPublished - 1 Dec 2016
Event24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sep 2016
Conference number: 24

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISSN (Print)2076-1465

Conference

Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016
CountryHungary
CityBudapest
Period29/08/162/09/16

Fingerprint

optical radar
waveforms
methodology
reflectance
wavelengths
lasers

Cite this

Altmann, Y., Maccarone, A., McCarthy, A., Buller, G. S., & McLaughlin, S. (2016). Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms. In 2016 24th European Signal Processing Conference (EUSIPCO) (pp. 513-517). (European Signal Processing Conference (EUSIPCO)). IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760301
Altmann, Yoann ; Maccarone, Aurora ; McCarthy, Aongus ; Buller, Gerald Stuart ; McLaughlin, Stephen. / Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms. 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. pp. 513-517 (European Signal Processing Conference (EUSIPCO)).
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title = "Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms",
abstract = "This paper presents a new Bayesian clustering method to analyse remotescenes sensed via multispectral Lidar measurements. To a firstapproximation, each Lidar waveform mainly consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. By sensing the scene at several wavelengths, we expect amore accurate target range estimation and a more efficient spectralanalysis of the scene. Thanks to its spectral classification capability,the proposed hierarchical Bayesian model, coupled with an efficientMarkov chain Monte Carlo algorithm, allows the estimation of depthimages together with reflectivity-based scene segmentation images.The proposed methodology is illustrated via experiments conductedwith real multispectral Lidar data.",
author = "Yoann Altmann and Aurora Maccarone and Aongus McCarthy and Buller, {Gerald Stuart} and Stephen McLaughlin",
year = "2016",
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Altmann, Y, Maccarone, A, McCarthy, A, Buller, GS & McLaughlin, S 2016, Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms. in 2016 24th European Signal Processing Conference (EUSIPCO). European Signal Processing Conference (EUSIPCO), IEEE, pp. 513-517, 24th European Signal Processing Conference 2016, Budapest, Hungary, 29/08/16. https://doi.org/10.1109/EUSIPCO.2016.7760301

Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms. / Altmann, Yoann; Maccarone, Aurora; McCarthy, Aongus; Buller, Gerald Stuart; McLaughlin, Stephen.

2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. p. 513-517 (European Signal Processing Conference (EUSIPCO)).

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

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AB - This paper presents a new Bayesian clustering method to analyse remotescenes sensed via multispectral Lidar measurements. To a firstapproximation, each Lidar waveform mainly consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. By sensing the scene at several wavelengths, we expect amore accurate target range estimation and a more efficient spectralanalysis of the scene. Thanks to its spectral classification capability,the proposed hierarchical Bayesian model, coupled with an efficientMarkov chain Monte Carlo algorithm, allows the estimation of depthimages together with reflectivity-based scene segmentation images.The proposed methodology is illustrated via experiments conductedwith real multispectral Lidar data.

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Altmann Y, Maccarone A, McCarthy A, Buller GS, McLaughlin S. Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms. In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE. 2016. p. 513-517. (European Signal Processing Conference (EUSIPCO)). https://doi.org/10.1109/EUSIPCO.2016.7760301