Figure 10: DTM model based on a hybrid raster struc-
ture. The modelled breaklines were integrated in the clas-
sification (filtering) process for DTM determination.
First tests of this method for 3D breakline modelling on
large data sets showed that this method can be success-
fully applied to unclassified ALS data. Nevertheless, one
can think of a lot of further developments in order to en-
hance its capability. Next to improvements in the weight
model (e.g. no strict separation of left and right points) a
combined adjustment of more than one patch pairs would
on one hand improve the capabilities of the breakline es-
timation in dense wooded areas (larger areas with only a
view terrain points can be bridged) and on the other hand
constrains between neighbouring patch pairs can lead to
a homogenisation of the resulting breakline. Potentially
for certain breaklines the surface model should be more
flexible in order to avoid errors caused by model deficits.
A big lack is that up to now the implemented algorithm
does not consider jump edges. On these edges the cou-
pling of both surface pairs (forcing an intersection of the
surface patches) must be cancelled. Instead of an intersec-
tion line two breaklines (one belonging to the lower and
one to the upper surface) must be determined in order to
allow a modelling of a step edge.
In the future a detailed quality analysis of the results of
the presented 3D modelling procedure will be performed in
order to recognise some deficits of the method and to get
an idea about the possible accuracy in respect to reference
data. Still the fully automated extraction of breaklines
from irregular distributed point clouds is not visible, but
some methods for automatisation do exist, which are able
to reduce the manual work. Finally, the importance of
breakline modelling must be stressed. Explicitly modelled
breaklines allow a high quality improvement within the
ALS classification (filtering) process for DTM determina-
tion and offer, next to a better morphological modelling,
a higher data reduction of the final models.
ACKNOWLEDGMENTS
This research has been supported by the Austrian Science
Foundation (FWF) under project no. P15789.
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