International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
4. BUILDING DESCRIPTION
This section presents a building description process which
reconstructs building outlines from each building “blob”.
Generic building shape is represented as a mosaic of convex
polygon. A set of linear cues is extracted by both data-driven
and model-driven approaches. The building “blobs” are
recursively intersected by those linear cues, which produces
a set of polygon cues. Finally, building outlines are
reconstructed by merging only “building” polygons forming
building objects.
4.1 Data-driven linear cue extraction
The first stage of the building description is to extract
boundary lines from Ikonos imagery with the support of the
RTF filtering result. Straight lines extracted by the Burns
algorithm (Burns et al., 1986) are filtered by a length
criterion, by which only lines larger than pre-specified
length threshold, /; =5m, remain for further processing.
Then, two rectangle boxes with certain width, /,=5m, are
generated along two orthogonal directions to the line vector
filtered in length. The determination of boundary line can be
given if non-building and building points are simultaneously
found in both boxes or if only building-label points are
found in one of the boxes and no lidar point can be found in
the other box. The latter boundary line condition is
considered if a low density lidar dataset is used. Figure 4
illustrates this.
O non-building point @ building point — line cue
Figure 4. Illustration of boundary line detection
I ? Mia Lom as
(a) extracted straight lines. (b) filtered boundary lines
Figure 5. Result of data-drive cue extraction
As a final line filtering process, a geometric disturbance
corrupted by noise is regularized over boundary lines. A set
of dominant line angles of boundary lines is analyzed from a
gradient-weighted histogram which is quantized in 255
discrete angular units. In order to separate a weak, but
significant peak from other nearby dominant angles, a
hierarchical histogram-clustering method is applied. Once
the dominant angle, 6, is obtained, lines with angle
discrepancies which are less than certain angel thresholds,
0,,-30?, from 0; are found. Then, their line geometries are
modified as their angles are replaced with 04. These
modified lines do not contribute to the succeeding dominant
angle analysis and the next dominant angle is obtained. In
this way, a set of dominant angles is obtained, by which
geometric properties of boundary lines can be regularized
(see figure 5).
4.2 Model-driven linear cue extraction
New line cues are "virtually" extracted from lidar space in
order to compensate for the lack of intensity line cue density
by employing specific building models. For each intensity
line cue, parallel lines and *U" structured lines are inferred
from lidar space. First, a box growing direction, pointing to
the location of parallel boundary line is determined. To this
end, a small virtual box is generated with a width of /,=5m
from the selected intensity line in the same way of detecting
boundary lines presented in 84.1. To that direction, the
virtual box grows until it comes across any on-terrain point
(see figure 6 (a)) Then, it de-grows in order to have
maximum building points while in its minimum size (see
figure 6 (b)). In this way, the virtual box is expanded, but at
this time, towards to two orthogonal directions to the
parallel boundary line detected (see figure 6 (c)). Thus, *U"
structured boundary lines made with the parallel boundary
line can be detected. Finally, these three virtual lines
detected are back-projected onto image space and then, their
line geometry is adjusted by gradient weighted least-square
method. Figure 6(d) shows model-driven cues extracted
from figure 5(b).
(c) (d)
© non-building point @ building point
Figure 6. Result of model-driven cue extraction
intensity line cue ==«= virtual line cue
4.3 Polygonal cue generation
Initial polygons resulting from the building detection result
of figure 3(d) are decomposed of a set of convex polygons by
a recursive intersection of linear cues, called hyperlines.
This polygonal segmentation is implemented by BSP
(Binary Space Partitioning) tree algorithm introduced by
Fuchs et al. (1980). Figure 7 illustrates the overall
partitioning scheme to generate polygons. Suppose that we
have an initial polygon with rectangle geometry, P^. wherein
LIDAR points are distributed with building and non-
building label. All vertices comprising P^ are stored as à
root node of BSP tree for further recursive partitioning (see
figure 7(a)).
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