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