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

Abstract

Recent advances in laser scanning hardware have allowed rapid generation of highresolution 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 bareearth 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.

An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

“…Furthermore, thick underbrush existed in the sites of Jack Pine and Open Area, which could further impede the LiDAR penetration to ground. As reviewed in Section 1, several advanced methods [9][10][11][12][13] were reported in the literature to generate more reliable terrain points for a given set of discrete points using local properties. These methods can be used together with the proposed method to further filter out falsely identified terrain points.…”

“…This might be because that weak echoes caused by noise were falsely taken as those detected by terrains. A better filtering methods, such as those proposed in [9][10][11][12][13] may help to remove false terrain points. It is worth mentioning that noise might be introduced by adding the weak echoes, which might negatively affect the accuracy of the generated DTM, especially in the flat areas.…”

“…A novel cloth simulation filtering was proposed in Zhang et al (2016) [13] that employed the nature of cloth and modified the physical process of cloth simulation to adapt to terrain point cloud. With the abovementioned methods, more and reliable ground points were generated compared with previously existing approaches [9][10][11][12][13]. However, the gap in the input LiDAR data resulting from such objects as dense vegetation canopies remains a significant challenge.…”

“…Specifically, an adaptive approach to employ different interpolation methods based on the complexity of local terrain was designed in Maguya et al (2013) [9]. Similarly, an adaptive threshold was employed in filtering non-ground points in Su et al (2015) [10]. A novel energy function balanced by adaptive ground saliency was used to adapt to steep slopes, discontinuous terrains, and complex objects in the filtering process to identify ground points in Hu et al (2015) [11].…”

“…One group is focused on developing advanced filtering and interpolation algorithms by fully exploiting local contextual information [9][10][11][12][13]. Specifically, an adaptive approach to employ different interpolation methods based on the complexity of local terrain was designed in Maguya et al (2013) [9].…”