A new hierarchical moving curve-fitting algorithm for filtering lidar data for automatic DTM generation

AbstractRecent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms. Additional informationFundingThis research was funded by the National Natural Science Foundation of China under the project 'Estimating the leaf area index of maize in whole growth period using terrestrial LiDAR data' [No. 41371327]; the young talents plan project 'Growth monitoring of maize using terrestrial LiDAR data' [No. YETP0316]; the National Science and Technology Support Program [No. 2012BAH34B01]; the National Natural Science Foundation of Shandong Province in China [No. ZR2009BQ017].