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Mapping without the sun
Zhang, Jixian

Figure 8. Simple (left) and separately simulated (right) multi
look image of the New Palace (Neues Schloss) in
3.6 Side-lobe visualization
Strong reflecting objects like comer reflectors can cause typical
blooming effects in SAR images. The blooming is caused by the
high amount of energy reflected back to the sensor. Analyzing
the simulation result, which is first rendered to a texture, strong
reflections are detected. Depending on a certain threshold, ref
lections are considered to cast side-lobes. Afterwards these side-
lobes are additively rendered to the simulation result, as de
picted in Figure 9.
Figure 9. SARViz simulation of the 3D-model of the „Stifts-
kirche“ in Stuttgart
Fusing SAR data and optical imagery can provide a variety of
new information, not available analyzing each data set sepa
rately. Using low or medium resolution data, the data fusion can
be done by simple methods. In flat terrains, even straightfor
ward geo-coding approaches are suitable. While analyzing ve
getation in images with about 25m ground resolution, the differ
ent geometries of the images are not crucial, because the lay
over of most vegetation related objects is influencing less than
one pixel.
But while analyzing today’s high-resolution images, this is not
true anymore. The spatial resolution of SAR sensors improved
tremendously during the last decade. The new TerraSAR-X pro
vides images with a spatial resolution of about one meter (Wer-
ninghaus, 2006), modem airborne systems achieve spatial reso
lutions in the decimeter scale (Ender & Brenner, 2003). In these
images, the geometrical effects caused by the distance geometry
of a SAR image are significant, especially in urban areas. The
appearance of buildings in high-resolution images differs from
the low-resolution appearance. Even small structures are visible
inside the layover.
The position, the shape and the radiometric appearance of any
object depends on the sensor position, the sensor properties and
the environment of the object. For a successful fusion of high-
resolution images from different sensor types, the sensor prop
erties as well as the 3D shape of the objects of interest should be
known. The spatial accuracy of this data has to be high, because
for any data fusion approach, the geo-coding accuracy should
match the data resolution (Soergel et al, 2006). If the shape is
available, additional information about the object properties can
be analyzed. Analyzing bridges for example, the outlines of a
bridge can be easily determined using aerial photos, whereas
deriving the outlines using SAR is difficult. Using the width
from the aerial image measurement and the true position of the
bridge from the double-bounce reflection, the real height of the
bridge can be determined easily (Soergel et al, 2007). For many
applications such approaches are not feasible. Remote sensing is
often used because the terrain and the objects are unknown. Any
remote sensing approach considering information about the 3D
terrain or shape as prerequisites is problematic.
Multi-sensor data fusion can be implemented as a multi-step
strategy. The chosen strategy depends on the application and the
available data. Assuming no change in the terrain or the 3D
shape between the different images, e.g. because the time differ
ence between the acquisitions is small, the 3D shape of the area
can be determined by one sensor and the data fusion is based on
the generated model. The 3D model can be generated by stan
dard remote sensing methods like interferometric SAR, LIDAR
or photogrammetry (Brenner, 2005). Also terrestrial data acqui
sition by terrestrial laser scanning, photogrammetry or video-
based reconstruction (Mordohai et al, 2007) is possible. Further
research could reveal a path for a direct generation of 3D mod
els from optical and SAR images, but this has not yet been pre
sented. In another approach, changes occurring between the im
age acquisition times are assumed. In this case, the 3D shape
should be generated from the data acquired earlier. Changes
which occur between the acquisition dates can be detected
based on the fusion of the data.
Assuming available stereo images, LIDAR or terrestrial data,
3D building models can be generated. The automated gener
ation of building models using LIDAR and GIS footprint infor
mation is a well-known approach (Haala & Brenner, 1999). The
building models in Figure 11 are reconstructed using this auto
mated method. Like any automated approach, some models are
not reconstructed correctly.
Figure 10. Erroneous reconstructed building models
If this erroneous reconstructed models are used for change de
tection applications, various false alarms will occur.
Figure 11. Subset of a DOSAR image of Karlsruhe (left) and
SARViz simulation of the area (right)
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