Processing Fine Digital Terrain Models by Markovian Regularization from 3D Airborne Lidar Data

Airborne laser scanner systems are based on the emitting/receipting cycle of a laser beam mounted on the nadir of an airplane. They provide 3D point clouds of the topography within an altimetric accuracy of generally less than 10 cm. The divergence of the emitted laser beam allows the recording of at least two distance measurements for each transmitted pulse. It is particularly interesting when surveying forest areas whereon both the top canopy and the ground are recorded at once. The aim of the paper is to describe a methodology for modelling the terrain from sparse laser ground points with a dense altimetric surface, without using classical interpolation algorithms. Our approach is based on the definition of an energy function that manages the evolution of a terrain surface in a Bayesian framework. The energy is designed as a compromise between a data attraction term and a regularization term. The minimum of this energy corresponds to the final terrain surface. We show some conclusive results of the retrieving of a realistic terrain.