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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Figure 2. Greenwich lidar DSM
3. BUILDING DETECTION
The complexity of building extraction process can be
reduced by a large amount if the process can be focused on
single building object. This section presents a building
detection method to localize individual buildings by
sequentially removing dominant urban features which are
not relevant to buildings.
3.1 Terrain detection
A lidar filter, called recursive terrain fragmentation (RTF)
filter, was developed to distinguish between on-terrain
points and off-terrain ones from a cloud of lidar points. The
RTF filter was implemented by employing a hypothesis-test
optimization in different scales. This filter assumes that
generic terrain surface is a mosaic of planar terrain surfaces.
The entire lidar space, initially hypothesized as a single
planar terrain surface, is recursively fragmented with small
sub-regions until the coexistence of different terrain slopes
cannot be found over all fragmented regions. More detailed
description of the RTF filter can be found in Sohn &
Dowman (2002). Figure 3(a) shows the on-terrain points
detected by the RTF filter from figure 2. In this figure, some
terrain segments which are not densely covered by the
filtered on-terrain points show poor quality of the Greenwich
lidar DSM.
32 High-rise and low-rise object detection
With the on-terrain points detected by the RTF filter, a
DTM is generated. Then, outlying points with a height less
than a pre-defined height threshold (4m) from the generated
DTM are classified as “high-rise” features, otherwise as the
“low-rise” ones (see figure 3(b)).
3.3 Tree detection
Since the “high-rise” feature class generally contains trees
as well as buildings, *vegetation" points must be identified.
The normalized difference vegetation indices (NDVI) are
computed by a combination of red and near-infrared
channels of Ikonos. When the “high-rise points" are back-
projected onto the NDVI map, a small mask (5x5 size) is
constructed around them, and “vegetation” points are
identified if any masked pixel has the NDVI value larger
than a threshold value (70.8) (see figure 3(c)).
34 Building blob detection
Isolating the building label points and making them into
individual building objects is rather straightforward. Those
points classified into the on-terrain, low-rise, and tree
objects are together assigned non-building labels. Then,
building points surrounded by the non-building labels, are
grouped as isolated objects. As a result, 28 building “blobs”
can be found from figure 3(d) after removing small “blobs”
whose member points are less than 30 points. Further
processing allows the individual building “blobs” to be
bounded with rectangle polygons, and these polygons are
then fed into the building description process, which will be
discussed in the next section.
d med re i ACUARIO
(b) “high-rise” point detection result
€
Rie Pe beth
(c) after removing “vegetation” points
| (d) building “blob” detection result
Figure 3. Building detection results