Robust spectral unmixing of multispectral Lidar waveforms

Yoann Altmann, Aurora Maccarone, Aongus McCarthy, Gregory Newstadt, Gerald Stuart Buller, Stephen McLaughlin, Alfred Hero

Research output: Contribution to conferencePaper

Abstract

This paper presents a new Bayesian spectral unmixing algorithm to
analyse remote scenes sensed via multispectral Lidar measurements.
To a first approximation, each Lidar waveform consists of the temporal
signature of the observed target, which depends on the wavelength
of the laser source considered and which is corrupted by Poisson
noise. When the number of spectral bands is large enough, it
becomes possible to identify and quantify the main materials in the
scene, on top of the estimation of classical Lidar-based range profiles.
Thanks to its anomaly detection capability, the proposed hierarchical
Bayesian model, coupled with an efficient Markov chain
Monte Carlo algorithm, allows robust estimation of depth images together
with abundance and outlier maps associated with the observed
3D scene. The proposed methodology is illustrated via experiments
conducted with real multispectral Lidar data.
Original languageEnglish
Publication statusPublished - 22 Aug 2016
Event8th Workshop on Hyperspectral Image and Signal Processing 2016: Evolution in Remote Sensing - UCLA, Los Angeles, United States
Duration: 21 Aug 201624 Aug 2016
Conference number: 8

Workshop

Workshop8th Workshop on Hyperspectral Image and Signal Processing 2016
Abbreviated titleWHISPERS 2016
CountryUnited States
CityLos Angeles
Period21/08/1624/08/16

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lidar
outlier
laser
anomaly
methodology

Cite this

Altmann, Y., Maccarone, A., McCarthy, A., Newstadt, G., Buller, G. S., McLaughlin, S., & Hero, A. (2016). Robust spectral unmixing of multispectral Lidar waveforms. Paper presented at 8th Workshop on Hyperspectral Image and Signal Processing 2016, Los Angeles, United States.
Altmann, Yoann ; Maccarone, Aurora ; McCarthy, Aongus ; Newstadt, Gregory ; Buller, Gerald Stuart ; McLaughlin, Stephen ; Hero, Alfred . / Robust spectral unmixing of multispectral Lidar waveforms. Paper presented at 8th Workshop on Hyperspectral Image and Signal Processing 2016, Los Angeles, United States.
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abstract = "This paper presents a new Bayesian spectral unmixing algorithm toanalyse remote scenes sensed via multispectral Lidar measurements.To a first approximation, each Lidar waveform consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. When the number of spectral bands is large enough, itbecomes possible to identify and quantify the main materials in thescene, on top of the estimation of classical Lidar-based range profiles.Thanks to its anomaly detection capability, the proposed hierarchicalBayesian model, coupled with an efficient Markov chainMonte Carlo algorithm, allows robust estimation of depth images togetherwith abundance and outlier maps associated with the observed3D scene. The proposed methodology is illustrated via experimentsconducted with real multispectral Lidar data.",
author = "Yoann Altmann and Aurora Maccarone and Aongus McCarthy and Gregory Newstadt and Buller, {Gerald Stuart} and Stephen McLaughlin and Alfred Hero",
year = "2016",
month = "8",
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language = "English",
note = "8th Workshop on Hyperspectral Image and Signal Processing 2016 : Evolution in Remote Sensing, WHISPERS 2016 ; Conference date: 21-08-2016 Through 24-08-2016",

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Altmann, Y, Maccarone, A, McCarthy, A, Newstadt, G, Buller, GS, McLaughlin, S & Hero, A 2016, 'Robust spectral unmixing of multispectral Lidar waveforms' Paper presented at 8th Workshop on Hyperspectral Image and Signal Processing 2016, Los Angeles, United States, 21/08/16 - 24/08/16, .

Robust spectral unmixing of multispectral Lidar waveforms. / Altmann, Yoann; Maccarone, Aurora; McCarthy, Aongus; Newstadt, Gregory ; Buller, Gerald Stuart; McLaughlin, Stephen; Hero, Alfred .

2016. Paper presented at 8th Workshop on Hyperspectral Image and Signal Processing 2016, Los Angeles, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Robust spectral unmixing of multispectral Lidar waveforms

AU - Altmann, Yoann

AU - Maccarone, Aurora

AU - McCarthy, Aongus

AU - Newstadt, Gregory

AU - Buller, Gerald Stuart

AU - McLaughlin, Stephen

AU - Hero, Alfred

PY - 2016/8/22

Y1 - 2016/8/22

N2 - This paper presents a new Bayesian spectral unmixing algorithm toanalyse remote scenes sensed via multispectral Lidar measurements.To a first approximation, each Lidar waveform consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. When the number of spectral bands is large enough, itbecomes possible to identify and quantify the main materials in thescene, on top of the estimation of classical Lidar-based range profiles.Thanks to its anomaly detection capability, the proposed hierarchicalBayesian model, coupled with an efficient Markov chainMonte Carlo algorithm, allows robust estimation of depth images togetherwith abundance and outlier maps associated with the observed3D scene. The proposed methodology is illustrated via experimentsconducted with real multispectral Lidar data.

AB - This paper presents a new Bayesian spectral unmixing algorithm toanalyse remote scenes sensed via multispectral Lidar measurements.To a first approximation, each Lidar waveform consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. When the number of spectral bands is large enough, itbecomes possible to identify and quantify the main materials in thescene, on top of the estimation of classical Lidar-based range profiles.Thanks to its anomaly detection capability, the proposed hierarchicalBayesian model, coupled with an efficient Markov chainMonte Carlo algorithm, allows robust estimation of depth images togetherwith abundance and outlier maps associated with the observed3D scene. The proposed methodology is illustrated via experimentsconducted with real multispectral Lidar data.

M3 - Paper

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Altmann Y, Maccarone A, McCarthy A, Newstadt G, Buller GS, McLaughlin S et al. Robust spectral unmixing of multispectral Lidar waveforms. 2016. Paper presented at 8th Workshop on Hyperspectral Image and Signal Processing 2016, Los Angeles, United States.