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

Yoann Altmann; Aurora Maccarone; Aongus McCarthy; Gerald Stuart Buller; Stephen McLaughlin

doi:10.1109/EUSIPCO.2016.7760301

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

Yoann Altmann, Aurora Maccarone, Aongus McCarthy, Gerald Stuart Buller, Stephen McLaughlin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

34 Downloads (Pure)

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 language	English
Title of host publication	2016 24th European Signal Processing Conference (EUSIPCO)
Publisher	IEEE
Pages	513-517
Number of pages	5
ISBN (Electronic)	9780992862657
DOIs	https://doi.org/10.1109/EUSIPCO.2016.7760301
Publication status	Published - 1 Dec 2016
Event	24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary Duration: 29 Aug 2016 → 2 Sep 2016 Conference number: 24

Publication series

Name	European Signal Processing Conference (EUSIPCO)
Publisher	IEEE
ISSN (Print)	2076-1465

Conference

Conference	24th European Signal Processing Conference 2016
Abbreviated title	EUSIPCO 2016
Country/Territory	Hungary
City	Budapest
Period	29/08/16 → 2/09/16

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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

@inproceedings{75dabb84441a4fefaac1b51d084b2cb8,

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",

month = dec,

day = "1",

doi = "10.1109/EUSIPCO.2016.7760301",

language = "English",

series = "European Signal Processing Conference (EUSIPCO)",

publisher = "IEEE",

pages = "513--517",

booktitle = "2016 24th European Signal Processing Conference (EUSIPCO)",

address = "United States",

note = "24th European Signal Processing Conference 2016, EUSIPCO 2016 ; Conference date: 29-08-2016 Through 02-09-2016",

}

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 proceeding › Conference contribution

TY - GEN

T1 - Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms

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AU - Maccarone, Aurora

AU - McCarthy, Aongus

AU - Buller, Gerald Stuart

AU - McLaughlin, Stephen

N1 - Conference code: 24

PY - 2016/12/1

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N2 - 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.

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.

U2 - 10.1109/EUSIPCO.2016.7760301

DO - 10.1109/EUSIPCO.2016.7760301

M3 - Conference contribution

T3 - European Signal Processing Conference (EUSIPCO)

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EP - 517

BT - 2016 24th European Signal Processing Conference (EUSIPCO)

PB - IEEE

T2 - 24th European Signal Processing Conference 2016

Y2 - 29 August 2016 through 2 September 2016

ER -

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

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