buildings utilizes both the orthoimages & DSMs data of the
previous epoch and existing building maps. In many previous
researches, change detection is performed mainly by comparing
the data of two different epochs at a feature level or object level.
For example, extracting linear features like edges or lines from
orthoimages and comparing them to building polygons, or
extracting building objects from orthoimages & DSMs data and
comparing them to building polygons based on calculating an
overlapped area percentage. The main difficulty of these
methods is that the performance of change detection depends on
the correctness and completeness of feature extraction and
object extraction, which is not that easy in a dense urban
environment like many metropolises. On the other hand, the
characteristics of different kinds of data, i.e. building polygon
and orthoimages & DSMs can be utilized and the two data can
be integrated. It should be noticed that the height of a building
is very effective information in order to handle building change.
If a building polygon and DSM is available, the ground altitude
around the building and also the altitude inside the building
polygon can be estimated. Thus, the height of the building at
the present epoch can be obtained. If the building is demolished,
the obtained height of the building should be very low and can
be detected easily. The flow chart of the proposed approach for
building map updating is shown in Figure 1.
| DSM & orthoimages | - 2D Building vector data |
5».
P»
A
* Local ground altitude
| Large buildings | around each building
Small buildings with Altitude of each building
gabled roofs
v Y
| Height of each building |
plane roofs
Demolished and
| Newly-built buildings e reconstructed buildings
Building map updating
Figure 1. Flow chart of building map updating
Small buildings with
2.1 Detection of newly-built buildings
In this study, newly-built buildings are detected based on
building object extraction from newly-acquired orthoimages &
DSMs (Zhu et al, 2008). The proposed approach applies a
hierarchical strategy to extract large buildings, small buildings
with gabled roofs, and small buildings with plane roofs,
respectively. First, a Normalized DSM (NDSM) is generated
mainly by morphological processing on DSM. Then, large
buildings which have large areas and high heights are extracted
by simply thresholding the NDSM followed by some
morphological processing. After that, small buildings with
gabled roofs are extracted by a Local Surface Normal Angle
Transform (LSNAT) method through the extraction of roof
plane. And then, a Marker Controlled Watershed Segmentation
(MCWS) method is applied to extract small buildings with
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
plane roofs. The final result of building object extraction is
acquired by merging the results obtained from above mentioned
steps. This result is compared with an existing building map,
and newly-built buildings can be separated out from the other
ones by retaining the building objects at the locations where
there were no building polygons in the existing building map.
2.1.1 Normalized DSM Generation
Morphological operators are used to remove the objects on the
ground, like buildings, trees, cars, and others since they are
proved to be suitable for such shape processing of these objects.
A morphological filter first performs a close operation to fill the
pits in the DSM. A close operator dilates the DSM first and
erodes it then. After that, the morphological filter performs an
open operation to remove the objects such as buildings, trees
and cars. The size of morphological element is decided by the
size of the maximum object to be removed. Since removing the
objects with a large scale element will cause step effects in a
DTM, a low pass filter is used to smooth the DTM in order to
remove step effects.
Then, the NDSM can be generated by subtracting the DTM
from the DSM. The NDSM refers to the ground surface that
suppresses the terrain height to an equal level. It gives the real
heights of the objects on the ground and can be segmented
according to a certain height threshold.
2.1.2 Building extraction using LSNAT method
Large buildings are extracted by thresholding a NDSM directly,
and morphologically processed in order to separate the objects
connected with each other and to remove some small areas.
From the NDSM, it is found that some buildings are quite small.
If a low threshold is simply used to binary them, there will be
large non-zero regions composed of several connecting
buildings. On the contrary, most buildings can simply be
segmented but the small ones will be lost. Therefore, a local
surface normal analysis is applied for roof plane extraction in
order to extract small buildings with gabled roofs.
In this study, a local quadratic surface least squares method is
used to obtain local surface normal vectors. Then the NDSM
can be transformed into two normal angles at each grid, which
represents the normal vector by directions. Then a 2D
histogram of the angles can be generated. The peaks of the 2D
histogram will correspond to the directions of concentrated
normal vector directions. The building roof grids with the same
normal direction will generate a peak in the histogram, and so
do other objects and ground grids, etc. By extracting the grids
corresponding to specific peaks, the planes corresponding to
certain roofs can be detected.
The 2D histogram generates approximately three main peaks.
Among the three peaks, the maximum value occurs at the center.
It implies that the directions of the normal vectors mostly go
upward. They represent the grids of the ground and plane roofs.
The other two peaks represent the grids of gabled roofs,
respectively.
If a building has a gabled roof, it should show at least a pair of
plane. Thus, small buildings with gabled roofs can be detected
by counting the numbers of grids corresponding to the two
