The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
4. 3D ROOFTOP MODEL GENERATION
4.1 Low Level Features Extraction
To detect 2D lines from epipolar image, edge detection is
carried out first and then 2D lines are formed from edges. We
employed Canny edge detector, since it is optimal according to
the criteria where edge is defined and comes up with thin edges.
To obtain 2D line segment, we use Boldt algorithm (Boldt,
1989) based on token grouping. The method extracts a basic
line element, token, in terms of the properties of line A and
construct 2D line using grouping process. It is efficient in
detecting 2D lines of large structure appeared in urban image.
4.2 Grouping and Filtering Processes
Suspected building regions are used to remove line segment that
outside or far from interested object boundaries meanwhile the
needed line segments are still kept for later processing steps.
Then, we group the closely parallel linear segments since they
usually represent a linear structure of objects in image, like the
border of a roof or the divider between ground terrain and
building, by using a “folding space” between two line segments.
If both line segments are inside the folding space, two line
segments can be replaced by a single line which orientation is
the longer line segment orientation and length is total length of
two segments. After this process, each group of the closely
overlapping and parallel line segments is represents only by one
single line.
4.3 Corner Detection
Comer is calculated as intersection of two line segments which
their angle is from 80° to 100° and one of them has nearest
distance to another one. We define four types of comer. They
are labeled as I, II, III and IV, as shown in Figure 4. Each
comer has an attribute to indicate whether it is L-junction or T-
junction. This attribute is used to decide whether two different
comers have a connection or not. For example, if a comer’s
label is I and type is L-junction, it connects to any type of
comer. However, it prefers connecting to a comer which label
is II or IV. If that comer is T-junction, it can only connect to a
comer which label is II or IV. This mle is used in hypothesis
generation to build collated features.
With the flexible connection between comers, our method is
able to detect rectilinear rooftops. Figure 5 show some
examples of comer detection, A, B, E, F, G are L-junctions
while C, D are T-junctions.
II
->111
V
IV
Figure 2 showed the typical case of closely parallel linear
segments grouping. These linear segments are or nearly parallel
lines. So the first condition is the angle between them should be
from 0° to 10°. If two line segments are fragmented lines from
one edge, these line segments must be close and should be
inside a folding space created by them.
The U shaped structure in Figure 3 is used to detect candidates
for rooftop hypothesis generation. Any line segment in a set of
parallel lines with aligned end is a U shaped structure candidate
which is kept as input for hypothesis generation, otherwise that
line segment will be removed.
Line formed Line segments
Figure 2. Folding space
Area searched fcr lines for forming
the base of the U stricture
z
Figure 3 U-structure
Figure 4. Comer labeling
E
F
Figure 5. Comer detection
4.4 Rooftop Hypothesis Generation
A collated feature is a sequence of perceptually grouped comers
and line segments. Here, collated features are constructed from
filtered line segments and comers obtained from the filtering
and grouping process. That reduces computational effort and
false hypotheses.
Hypotheses are formed by alternation of comers and line
segments that form collated features. In a collated feature, two
comers have connectivity only if they satisfy the comer relation
condition and they are the nearest appropriate comer to each
other. Beside, every comer connects to only one comer on each
its line segment direction. Hypothesis generation is performed
by constructing the feature graph. Construction of the graph can
be seen as placing comers as nodes and edges between nodes if
there is the relation between the corresponding comers in the
collated features. When a node is inserted into the graph, the
system looks into the remaining nodes whether any node has
the relation with the inserted node. If some nodes satisfy the
connectivity relation rules, those nodes are inserted into the