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Technical Commission VII

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peaks of the 2D histogram within a watershed region. The ones
with grids greater than a certain value are regarded as a building.
2.1.3 Building extraction using MCWS method
Since some small buildings with very small areas, low heights
and plane roofs are easily misdetected, the MCWS method,
which is an improved method based on general watershed
segmentation, is applied.
Watershed segmentation to gray level images is suitable to
separate different objects from each others. But it will not give
the actual boundaries of each object. The boundary between
two objects by watershed segmentation just locates somewhere
between them, not exactly the object contour.
Watershed segmentation to gradient image can solve this
problem and give a real contour of the object. If we apply
watershed segmentation to a gradient image, the catchment
basins will be the dark regions of the gradient image, which
should theoretically correspond to the homogeneous grey level
regions. The watershed segmentation will stop at the contours
of the dark objects in the gray level image.
However, in practice, this transform produces an important
over-segmentation due to noise or local irregularities in the
gradient image. To avoid the over-segmentation, a marker
controlled watershed is introduced (Gao et al., 2001, Salembier
et al., 1994). Here, the watershed segmentation is implemented
to the gradient of NDSM (GNDSM). A marker is an area which
is the initial of a catchment basin. By giving each object and the
background a marker, and making them the catchment bases,
the desired objects can be segmented from the background.
Buildings, trees, and other off-terrain objects are taken as the
foreground objects and are assigned the foreground markers. A
foreground marker is a spot. If it is the catchment basin for the
gradient then the marker will grow to an object. There will be
as many objects as foreground markers.
The foreground marker is detected by local maxima. The local
maximum of an object may be a spot with certain area as a
marker. For the buildings with flat roofs, all the pixels in the
roofs will be detected as the local maximums in the ideal case.
In practice, most pixels of the roof, especially in the center, will
be detected as the marker spot. For some objects, because they
have more than one obstruction in the roof, there will be several
markers detected and consequently they will be segmented as
several objects. This disadvantage can be avoided by merging
the large regions with the foreground markers.
The ground of NDSM is taken as the background and is
assigned the background marker. Because the watershed of the
segmentation of the NDSM generally locates between objects,
it is initially taken as the marker of the background. Sometimes
the background marker crosses large regions so that these
objects will grow to the background. A refined procedure is
implemented to maintain these foreground markers.
2.1.4 Extraction of newly-built buildings
The final result of building object extraction can be acquired by
merging the results derived by the MCWS method with that of
the LSNAT method. Then, newly-built buildings can be
detected by overlying the results of extracted buildings on
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
building map. Newly-built buildings can be detected as
extracted building objects where there is no building in the
building map.
2.2 Detection of demolished and reconstructed buildings
Height information is one of the most important characteristics
of the objects on the ground, and shows the status of ground
surface. It is also very effective information in order to handle
building change, and should be utilized for building change
detection. In this study, detection of demolished and
reconstructed buildings is performed based on estimating the
height of each building using existing building maps and newly-
acquired DSMs. Figure 2 shows the illustration of building
height estimation. The proposed approach is explained in a step
wise procedure below.

Inner polygonal

Outskirt buffer

Figure 2. Illustration of building height estimation
2.2.1 Estimation of local ground altitude
The first step for building height estimation is to acquire the
local ground altitude surrounding each building. Based on the
building map data, an outskirt buffer around a building polygon
is generated. Then, the minimum height inside the outskirt
buffer is explored to acquire the altitude of the local ground
area around the building.
2.2.2 Estimation of building altitude
The altitude of each building also needs to be acquired.
Considering the uncertainty involving the gap between a
building polygon and DSM, and the quality of DSM data at the
border of a building, etc., an inner polygonal buffer is created
inside the building polygon. Then, the altitude of inner
polygonal buffer is acquired by exploring the minimum height
of the inner polygonal buffer. This altitude corresponds to the
altitude of the building polygon.
2.2.3 Building height estimation
Next, the height of the building can be estimated directly by
subtracting the altitude of the building polygon from the ground
altitude surrounding the building.
2.2.4 Extraction of demolished and reconstructed buildings
Finally, by comparing the building height with a pre-defined
threshold, the status of the building, i.e. unchanged, demolished
or reconstructed can be detected. If a building is demolished or
reconstructed, the obtained building height should be very low
hence can be simply detected.