Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes
Open Access
- 1 January 2019
- journal article
- research article
- Published by Scientific Research Publishing, Inc. in Advances in Remote Sensing
- Vol. 08 (02), 51-75
- https://doi.org/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.Keywords
This publication has 17 references indexed in Scilit:
- State-of-the-Art: DTM Generation Using Airborne LIDAR DataSensors, 2017
- Augmentation of vertical accuracy of digital elevation models using Gaussian linear convolution filterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Dual-directional profile filter for digital terrain model generation from airborne laser scanning dataJournal of Applied Remote Sensing, 2014
- A Point Clouds Filtering Algorithm Based on Grid Partition and Moving Least SquaresProcedia Engineering, 2012
- A methodology for processing raw LiDAR data to support urban flood modelling frameworkJournal of Hydroinformatics, 2011
- DEM Development from Ground-Based LiDAR Data: A Method to Remove Non-Surface ObjectsRemote Sensing, 2010
- The DGPF-Test on Digital Airborne Camera Evaluation Overview and Test DesignPhotogrammetrie - Fernerkundung - Geoinformation, 2010
- Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical IssuesRemote Sensing, 2010
- Comparison of Three Algorithms for Filtering Airborne Lidar DataPhotogrammetric Engineering & Remote Sensing, 2005
- Extracting urban features from LiDAR digital surface modelsComputers, Environment and Urban Systems, 2000