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Proceedings Paper

Using Bayesian networks to estimate missing airborne laser swath mapping (ALSM) data
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Paper Abstract

Land surface elevation measurements from airborne laser swath mapping (ALSM) data can be irregularly spaced due to occlusion by forest canopy or scanner and aircraft motion. The measurements are usually interpolated into a regularly spaced grid using techniques such as Kriging or spline-interpolation. In this paper a probabilistic graphical model called a Bayesian network (BN) is employed to interpolate missing data. A grid of nodes is imposed over ALSM measurements and the elevation information at each node is estimated using two methods: 1) a simple causal method, similar to a Markov mesh random field (MMRF), and 2) BN belief propagation. The interpolated results of both algorithms using the maximum a posteriori (MAP) estimates are presented and compared. Finally, uncertainty measures are introduced and evaluated against the final estimates from the BN belief propagation algorithm.

Paper Details

Date Published: 18 May 2006
PDF: 11 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 623404 (18 May 2006); doi: 10.1117/12.665341
Show Author Affiliations
J. Tory Cobb, Naval Surface Warfare Ctr. (United States)
Kittipat Kampa, Univ. of Florida (United States)
K. Clint Slatton, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)

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