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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Figure 1: Classification of laser points into classes bare-
Earth (blue (middle grey)), building (red (dark
grey)), and vegetation (green (light grey)).
Classified as
Ground Bare Building | Vegetation | Total
truth Earth
Bare Earth 41956 42 113 42111
Building 193 30368 5018 35579
Vegetation 2294 2764 17580 22638
Total 44443 33174 22711 | 100328
Table 1: Classification results in number of points
Classified as
Ground Bare Building | Vegetation | Total
truth Earth
Bare Earth 99,6 0,1 0,3 100,0
Building 0,5 85,4 14,1 100,0
Vegetation 10,1 12.2 77 7 100,0
Table 2: Classification results in percentages.
* One building and larger parts of another building were
classified as a vegetation segment. These buildings had
relatively small steep roof faces. With the point distance
of 1.2 m, the roughness attribute was similar to those of
vegetation segments. With a higher point density, one
could probably obtain a better distinction.
* The classification of the small segments is relatively
unreliable. The number of points within these segments
was often too low to generate representative attribute
values.
In a post-processing step these small errors could
easily be repaired. It can be argued that small vegetation
segments that are rest on building segments should also
be building segments. Similarly, small building segments
that are surrounded by bare Earth points and vegetation
points also need to be reclassified.
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Figure 2: Ground truth for those points of Figure 1 that
were classified incorrectly. See Figure 1 for the
legend.
e Points on walls of buildings were also often incorrectly
classified as vegetation points. These points stand out
clearly in Figure 2. Wall points accounted for about 80%
to the 5018 building points classified as vegetation. A
correction in a post-processing step could therefore
significantly improve the classification accuracy.
However, for the change detection this is of less
importance, since the sizes of the building segments will
not increase when the wall points are included.
e Some patches of vegetation adjacent to buildings were
grouped in segments with building points and classified
as building. Such errors may impact the change detection
as it may be concluded that a building has been extended.
A larger point density in combination with stricter
thresholds in the profile segmentation may reduce the
number of these errors. The stricter thresholds will result
in smaller segments. The increased point density is then
required to enable the reliable computation of the
segment roughness.
e The large area of vegetation classified as bare Earth near
the bottom of Figure 2 is an area with very low
vegetation that was merged with the surrounding bare
Earth.
5. CHANGE DETECTION
Even if the topographic database is up to date and buildings
are correctly extracted from the laser data, differences will
exist between the database objects and the extracted building
segments. These differences need to be taken into account
during the change detection. Otherwise, many unchanged
buildings will be presented to the operator for updating.
Several reasons for differences between database objects and