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 language | English |
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Pages (from-to) | 5525-5534 |
Number of pages | 10 |
Journal | IEEE Transactions on Signal Processing |
Volume | 63 |
Issue number | 20 |
Early online date | 16 Jul 2015 |
DOIs | |
Publication status | Published - 15 Oct 2015 |
Keywords
- stat.ME
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Stephen McLaughlin
- School of Engineering & Physical Sciences, Institute of Sensors, Signals & Systems - Professor
- School of Engineering & Physical Sciences - Professor
Person: Academic (Research & Teaching)
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Andrew Michael Wallace
- School of Engineering & Physical Sciences - Professor Emeritus
Person: Emeritus