Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

Fahmy F F Asal

doi:10.4236/ars.2019.82004

Abstract

Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.

Highlights

Digital Terrain Model (DTM) of a specific area represents the ground surface elevations in that area
Three filtering approaches for stripping off above ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter at varying window sizes have been applied on airborne Light Detection And Ranging (LiDAR) Digital Surface Models (DSMs) for creation of a reliable DTM since a DTM can be involved a wide range of environmental and engineering applications
A dataset of airborne LiDAR data of the ISPRS WG III/4 Test Project on Urban Classification and 3D Building Reconstruction that was captured over Vaihingen in Germany over a pure residential area with small detached houses on 21 August 2008 by Leica Geosystems has been used in the study

Summary

Introduction

Digital Terrain Model (DTM) of a specific area represents the ground surface elevations in that area. 2018 [1] carried out a study that aimed to evaluate the performance of four ground filtering algorithms for DTM extraction from airborne LiDAR measurements in a forest environment of distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions; closed-canopy forest, intermediate-canopy forest, and open-canopy forest), where the tested four ground filtering techniques are: weighted linear least squares, multi-scale curvature classification, progressive morphological filter and progressive triangulated irregular network. Sharma et al 2010, [4] acknowledged that the topography and land cover determine infiltration, runoff, and erosion processes on watershed, time modeling and routing of surface water and erosion are determined by the digital elevation data that can be obtained from high-resolution ground-based LiDAR They used a slope threshold and a focal mean filter method to remove vegetation and create bare earth DTMs and recommend that validations of the methods show vertical error of ±7.5 mm in the final DTM. They recommended that differences in flood depths of 40% were noticed between a model basing on a DTM extracted by the progressive morphological filtering algorithm and the predictions of other models

Research Aims and Objectives

Comparative Analysis of the Results

Conclusions