The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
487
There are some advantages on filtering of the laser images using
geodesic image reconstruction:
• The process is not sensitive to the size of the objects to be
filtered. Spacious as well as small buildings can be filtered
using this approach.
• Contrary to standard morphological processing, for which
proper structuring elements have to be defined this is not the
case in this process. In geodesic dilation the marker image
is dilated by an elementary isotropic structuring element.
• Another benefit is that, the geodesic image reconstruction
does not effect ground pixels. Therefore the normalized
DSM can be simply segmented using a threshold value of
zero.
• The filtering approach based on geodesic dilation is rela
tively fast. In many cases even in hilly regions the filtering
can be implemented with a single marker image. A marker
image which represents the minimum height value of the
mask image except pixels at the boundary when marker =
mask (Arefi et al., 2007b) can be used.
(b) DTM generation result (LODO) plus contour lines superimposed
on it
Figure 3: Generation of digital terrain model by hierarchical fil
tering of non-ground objects
4 BUILDING OUTLINE DETECTION AND
APPROXIMATION FOR GENERATING 3D
PRISMATIC MODEL - LODI
The normalized DSM shown in figure 2(d) contains buildings
as well as vegetation pixels and other 3D objects might be also
present in the data. Classification of the regions is carried out
rule based using geometric and other region properties. Size of
the regions, vegetation index based on first and last pulse range
data and variance of the surface normals have been employed in
rule based classification to separates building and vegetation re
gions. To model the second level of detail the extracted build
ing outline is simplified to a polygon which includes only few
significant points such as corners. For this purpose two meth
ods are employed: fitting a rectilinear polygon by iterative fitting
of minimum bounding rectangles (MBR) and straight line fitting
and merging based on RANSAC (Arefi et ah, 2007a). The first
method is simple and relatively fast to find the best rectilinear
polygon but is only applicable on rectangular polygons. First
1st approximation 1st approximation - Model of Surplus
Original (Superset) Original Regions
1st approximation - model
of surplus regions
2nd Approximation Original - 2nd
(Subset) Approximation
Figure 4: Iterative process of MBR for building outline approxi
mation
Figure 5: Building approximation result
the main orientations of the building edges are determined using
a Hough transform. The iterative process of applying MBR’s is
shown in figure 4. The process stops if the remaining unmodeled
details are neglectable. A result of such a MBR approximation
is shown in figure . If the analysis in Hough space indicates that
there is more than one main orientation (cf. .6) the second tech
nique is used. The example shown in figure illustrates that the left
building has a single main orientation represented by the red lines
and the right building has two main orientations represented by
red and blue lines. Accordingly, outline polygons are extracted
and approximated with MBR or the RANSAC method.
To generate the 3D model from 2D polygons the 2 component
of the polygon nodes is extracted from the DTM and averaged.
A representative height of the building is found by averaging the
heights of the LIDAR points inside the boundary polygon. Next,
the polygons relating to the walls and floor of each building are
formed. All 3D polygons are overlaid on DTM to create LODI
representation.