The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
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to derive hypotheses of building instances. The generated
hypotheses are based on several point attributes as well as the
spatial relationships among the non-ground points. More
specifically, non-ground points are classified into points
pertaining to planar or rough surfaces. Using an adaptive local
neighborhood for each point, one can decide whether this
neighborhood defines a planar surface or not. Then, a grouping
technique is applied to collect neighboring points that belong to
planar surfaces while considering proximity of them in 3D
space. Finally, the derived groups are filtered to generate
building hypotheses based on the size of the group and their
height above the terrain. The generated hypotheses are based on
the prior knowledge that buildings are usually large in size with
a certain minimum height above the ground. Figure 5 shows the
building hypotheses generated through these procedures. In the
figure, the points with different colours belong to the different
building hypotheses. One should note that the points belonging
to the different hypotheses might be shown in the same color
due to the limitation of the number of utilized colors. In
addition, a single hypothesis might consist of points from
several planes. This situation happens when a structure is
formed by a series of connected planes with different slopes and
aspects.
Figure 5. Generated building hypotheses.
2.3 Segmentation of Planar Patches and Intermediate
Boundary Generation
The following procedure segments the points that are in a single
building hypothesis, but may come from physically different
planes, into a group of planar patches. The proposed
segmentation technique in this paper is a voting scheme that
keeps track of the point attributes, as defined by an adaptive
local plane through its neighboring points, in an accumulator
array. More specifically, the procedure is composed of three
sub-steps: neighborhood definition; attribute derivation; and
clustering of neighboring points with similar attributes. First, a
neighborhood definition which considers both the three-
dimensional relationships between LiDAR points and the
physical shapes of surfaces is introduced and employed (Filin
and Pfeifer, 2006). The physical shapes of the surfaces on
which associated points are located are incorporated into the
neighborhood definition. This means that points located on the
same surface are considered to be possible neighbors, while
taking into account the proximity of the points. Points on
different surfaces, on the other hand, are not considered to be
neighbors, even if they are spatially close. This definition
increases the homogeneity among neighbors. Neighbors are
determined using a cylinder whose axis direction changes
according to the physical shape of the object in question. It is
for this reason that this neighborhood definition is referred to as
the adaptive cylindrical neighborhood definition. In this
research, point attributes are computed based on the
neighboring points identified using the adaptive cylinder
method. More specifically, each point has two attributes. These
attributes are the normal distances between the local plane
(which is defined by neighboring points through an adaptive
cylindrical neighborhood definition) and two pre-defined points,
shown as origin 1 and origin 2 in Figure 6. In addition, the
figure illustrates the basic concept of point attributes
computation.
Figure 6. Basic concept of point attributes computation.
Once the attributes for all the points are computed, they are
stored in an accumulator array that keeps track of the frequency
of such attributes. As might be expected, points with similar
attributes will lead to peaks in the accumulator array. Figure 7
and 8 shows the examples of the scanned LiDAR points and the
produced accumulator array using their attributes, respectively.
The LiDAR points in this area represent a gable roof, which
consists of two planes with different slopes. The computed
attributes from these points are voted in the accumulator array.
Two groups of similar attributes, produced from the points on
two different roof planes, construct two high peaks in the array.
Figure 7. Scanned LiDAR points from the area including a
gable roof.