Spectral Unmixing of Multispectral Lidar Signals

Research output: Contribution to journalArticle

Abstract

In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.
Original languageEnglish
Pages (from-to)5525-5534
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume63
Issue number20
Early online date16 Jul 2015
DOIs
Publication statusPublished - 15 Oct 2015

Fingerprint

Optical radar
Signal analysis
Markov processes
Monte Carlo methods

Keywords

  • stat.ME

Cite this

@article{7cb4fd6f37da4c9488eacbb15b933c41,
title = "Spectral Unmixing of Multispectral Lidar Signals",
abstract = "In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.",
keywords = "stat.ME",
author = "Yoann Altmann and Andrew Wallace and Steve McLaughlin",
year = "2015",
month = "10",
day = "15",
doi = "10.1109/TSP.2015.2457401",
language = "English",
volume = "63",
pages = "5525--5534",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "IEEE",
number = "20",

}

Spectral Unmixing of Multispectral Lidar Signals. / Altmann, Yoann; Wallace, Andrew; McLaughlin, Steve.

In: IEEE Transactions on Signal Processing, Vol. 63, No. 20, 15.10.2015, p. 5525-5534.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Spectral Unmixing of Multispectral Lidar Signals

AU - Altmann, Yoann

AU - Wallace, Andrew

AU - McLaughlin, Steve

PY - 2015/10/15

Y1 - 2015/10/15

N2 - In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.

AB - In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.

KW - stat.ME

U2 - 10.1109/TSP.2015.2457401

DO - 10.1109/TSP.2015.2457401

M3 - Article

VL - 63

SP - 5525

EP - 5534

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 20

ER -