Terrain Surface Reconstruction by the Use of Tetrahedron Model with the Mdl Criterion

A LIDAR filtering technique is used to differentiate on-terrain points and off-terrain points from a cloud of 3-D point data collected by a LIDAR system. A major issue of concern in this low-level filter is to design a methodology to have a continual adaptation to terrain surface variations. To this end, several essential observations are discussed in this paper: i) the terrain surface can be fragmented into a set of piecewise “homogeneous” plane surfaces, in which terrain surface variations are smoothed out, ii) a criterion for differentiating onand off-terrain point from plane terrain surface can be equivalently applied to these terrain segments assumed as being plane, and iii) an interand intra-relationship of onand off-terrain points can be as verifying the a priori taken assumption of the plane terrain surface. The main strategy implemented in our LIDAR filtering technique is to iteratively generate a number of terrain surface models in order to hypothesize and test a plane terrain surface over a local area. Finally, the most reliable plane terrain surface model is selected as an optimised solution and thus the terrain surface model is refined. To this end, we devise a two-step divide-and-conquer triangulation in terms of downward and upward model refinement; in this framework, a tetrahedron is used in order to hypothesize a plane terrain surface and the Minimum Description Length (MDL) criterion is employed for the selection of an optimized plane terrain surface model. The useful characteristics of this method are discussed with results derived from real LIDAR data.