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Mapping without the sun
Zhang, Jixian

the outlines of a
il photos, whereas
:. Using the width
rue position of the
; real height of the
1, 2007). For many
Remote sensing is
are unknown. Any
ition about the 3D
ed as a multi-step
application and the
terrain or the 3D
use the time differ-
) shape of the area
fusion is based on
generated by stan-
2trie SAR, LIDAR
restrial data acqui-
immetry or video-
s possible. Further
ration of 3D mod-
s not yet been pre-
ig between the im-
ase, the 3D shape
1 earlier. Changes
- can be detected
or terrestrial data,
automated gener-
HS footprint infor-
renner, 1999). The
;d using this auto-
, some models are
ding models
ed for change de-
irlsruhe (left) and
In Figure 11 the real DOSAR image and the SARViz simulation
of the erroneously reconstructed test area are depicted. DOSAR
is the multi-frequency polarimetric airborne SAR system of the
EADS Domier GmbH (Hoffmann & Fischer, 2002). The off-na-
dir angle is 70° and the 3dB-resolution is about 0.57m. For test
ing purposes an artificially changed model, depicted in Figure
12, has been used. The black building has been added to the
model to test the proposed change detection algorithm.
Figure 12. Changed 3D model
The detected changes are visible in Figure 13. Not only the
changes from the artificially inserted house are visible, but addi
tionally some changes in the centre of the building block are
visible and even larger differences are detected in the south-east
comer of the building block. These changes are directly related
to the incomplete and erroneous city model illustrated in Figure
Figure 13. Detected changes of the building block depicted in
Figure 12 overlaying a DOSAR image
As already presented before (Balz, 2004), the false alarms are
outweighing the detected changes. For automated change detec
tion based on simulation assisted fusion of automatically gener
ated 3D data, the building reconstruction process is crucial.
More reliable building models can be reconstructed using semi
automatic reconstruction systems. The “CyberCity Modeler”
published by Griin and Wang (1998) is commercially available.
Using the semi-automated reconstruction the false alarms based
on wrongly reconstructed models will be reduced. Due to gene
ralizations in the reconstruction process and objects which are
not included in the reconstructed building models, e.g. trees or
parking cars, still various false alarms will occur.
Semi-automated change detection or alarm verification proces
ses are another way of reducing false alarms. Assumed changes
can be presented together with the model and the real image,
clarifying the reason for the assumed change. Errors in the
building model as well as possible false detections due to the
object environment can be analyzed fast and reliable by human
Figure 14. Original DOSAR image (left) and SARViz simula-
tion (right) of the modified 3D model from Figure
Comparing the real image and the simulation separately, errors
and changes are not easily recognizable. For example in Figure
14, the erroneous building added to the model cannot be found
easily. In Figure 15, the simulation is overlaying the real SAR
image in real-time. The transparency is selectable and the simu
lation parameters can be changed at any time. Using this way of
data visualization, changes can be detected easily and reliably.
Figure 15. SAR image overlaid by a SARViz simulation
Changes and effects of assumed changes on the acquired SAR
or optical image can be analyzed in real-time using visualization
applications. False alarms due to erroneous building models can
be detected, for example, by visually comparing aerial images
of the area overlaid by computer visualizations of the building
models. This way of false alarm detection and quality control is
depicted in Figure 16.
Figure 16. Comparison of the aerial image (left) the image over
laid by the model (middle) and the oblique view of
the building model (right)
Interactive and simulation based change detection is a reliable
way of analyzing SAR data in urban areas. Beside the detection
of changes, the dimension of the change can even be measured
by interactively adapting the shape of the model and visually
comparing the simulation of the adapted model and the real
SAR image. Thanks to the real-time simulation capability, this
can be done fast and user-friendly.