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

J. Tory Cobb; Kittipat Kampa; K. Clint Slatton

doi:10.1117/12.665341

18 May 2006 Using Bayesian networks to estimate missing airborne laser swath mapping (ALSM) data

J. Tory Cobb, Kittipat Kampa, K. Clint Slatton

Proceedings Volume 6234, Automatic Target Recognition XVI; 623404 (2006) https://doi.org/10.1117/12.665341
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

J. Tory Cobb, Kittipat Kampa, and K. Clint Slatton "Using Bayesian networks to estimate missing airborne laser swath mapping (ALSM) data", Proc. SPIE 6234, Automatic Target Recognition XVI, 623404 (18 May 2006); https://doi.org/10.1117/12.665341

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