Robust spectral unmixing of multispectral Lidar waveforms

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

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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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  • 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.