Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields
Abstract
Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the main materials in the scene, in addition to estimation of the Lidar-based range profiles. Due 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 acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).
- Authors:
-
[1];
[1];
- Heriot-Watt Univ., Edinburgh, Scotland (United Kingdom). School of Engineering and Physical Sciences
- Google Inc., Pittsburgh, PA (United States)
- Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Electrical Engineering and Computer Science
- Publication Date:
- Research Org.:
- Univ. of Michigan, Ann Arbor, MI (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1367663
- Grant/Contract Number:
- NA0002534; EP/J015180/1; EP/K015338/1; EP/M01326X/1; W911NF-14-1-0479
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Computational Imaging (CD-ROM)
- Additional Journal Information:
- Journal Name: IEEE Transactions on Computational Imaging (CD-ROM); Journal Volume: 3; Journal Issue: 4; Journal ID: ISSN 2334-0118
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Multispectral Lidar; Depth imaging; Robust spectral unmixing; Anomaly detection; Markov Chain Monte Carlo
Citation Formats
Altmann, Yoann, Maccarone, Aurora, McCarthy, Aongus, Newstadt, Gregory, Buller, Gerald S., McLaughlin, Stephen, and Hero, Alfred. Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields. United States: N. p., 2017.
Web. doi:10.1109/TCI.2017.2703144.
Altmann, Yoann, Maccarone, Aurora, McCarthy, Aongus, Newstadt, Gregory, Buller, Gerald S., McLaughlin, Stephen, & Hero, Alfred. Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields. United States. https://doi.org/10.1109/TCI.2017.2703144
Altmann, Yoann, Maccarone, Aurora, McCarthy, Aongus, Newstadt, Gregory, Buller, Gerald S., McLaughlin, Stephen, and Hero, Alfred. Wed .
"Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields". United States. https://doi.org/10.1109/TCI.2017.2703144. https://www.osti.gov/servlets/purl/1367663.
@article{osti_1367663,
title = {Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields},
author = {Altmann, Yoann and Maccarone, Aurora and McCarthy, Aongus and Newstadt, Gregory and Buller, Gerald S. and McLaughlin, Stephen and Hero, Alfred},
abstractNote = {Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the main materials in the scene, in addition to estimation of the Lidar-based range profiles. Due 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 acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).},
doi = {10.1109/TCI.2017.2703144},
journal = {IEEE Transactions on Computational Imaging (CD-ROM)},
number = 4,
volume = 3,
place = {United States},
year = {Wed May 10 00:00:00 EDT 2017},
month = {Wed May 10 00:00:00 EDT 2017}
}
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Works referencing / citing this record:
Quantum-inspired computational imaging
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- Science, Vol. 361, Issue 6403
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- Optics Express, Vol. 27, Issue 4