A model‐based approach for reconstructing a terrain surface from airborne LIDAR data

Abstract

AbstractA lidar filtering technique is used to differentiate on‐terrain points and off‐terrain points from a cloud of 3D point data collected by a lidar system. A major issue of concern in this low‐level filter is to design a methodology to ensure a continual adaptation to variations of terrain slopes and object scales. In this paper, a new lidar filtering technique which hierarchically fragments lidar data into piecewise planar terrain models is introduced. Once a number of hypothetical planar terrain models are generated to fit the terrain surface of the underlying area, the optimal terrain model to produce the minimum labelling errors is determined based on minimum description length (MDL) principles. This hypothesis‐verification optimisation is achieved in a coarse‐to‐fine strategy by which the entire terrain surface is incrementally reconstructed by increasing the number of planar terrain models fitted. The proposed technique was successfully applied to a digital surface model provided within an OEEPE lidar trial, showing 0·94% of Type I errors and 6·75% of Type II errors compared to manually classified reference data.