Full text: Mapping without the sun

Timo Balz 
Institute for Photogrammetry (ifp), Universität Stuttgart, Germany 
Geschwister-Scholl-Strasse 24D, D-70174 Stuttgart 
Commission II WG 1, Commission VII WG 6 & 7 
KEY WORDS: SAR, Real-time, Simulation, Change Detection, Fusion 
SAR simulators are important tools for developing new SAR systems as well as for supporting the analysis of acquired SAR data. 
Using modem graphics cards for SAR simulation, even complex environments can be simulated in real-time. This is realized by 
implementing SAR geometry and radiometry within standard graphics hardware, which nowadays offers 3D hardware acceleration 
and programmable graphics processing units (GPU). The geometric differences between optical and SAR images are the biggest 
challenge for any multi-sensor data fusion approach. High-resolution data fusion should be based on 3D models, in urban areas pro 
vided by city models. Differences between the model and the newly acquired data can be detected by comparing the simulation of the 
data with the acquired data. Due to unreliable models and inaccurate simulation results, automatic approaches provide various false 
alarms. Semi-automatic approaches are more reliable alternatives. 
The time needed to calculate a SAR simulation is not crucial for 
most scientific applications or for applications in sensor design. 
For these applications, the SAR simulation has to be as realistic 
as possible. A totally different application scenario is the real 
time visualization of SAR effects. Here the realism of the simu 
lation is not crucial, but the visualization must be in real-time. 
Real-time SAR simulations are useful for interactive applica 
tions, like mission planning or applications in training and edu 
cation. Furthermore, simulation assisted change detection and 
data fusion benefit from simulation results in real-time. 
Radar images differ in many ways from images acquired by 
passive sensor systems. Any data fusion approach has to consid 
er these differences. The combination of SAR simulation and 
visualization provides new methods for model based data fu 
sion. Based on 3D models, e.g. city models, differences between 
the model and the newly acquired data can be detected by com 
paring the simulation of the models with the acquired data. Data 
fusion between high-resolution SAR and optical images must 
consider the terrain and the 3D shape of the objects of interest. 
Fusing an agricultural area for vegetation classification in a low 
resolution, the topography of the area is negligible. Using high- 
resolution images in mountainous or urban areas, the terrain and 
object shapes have to be taken into consideration. This requires 
3D models of the area of interest. Beside the problem of 
availability, the quality and reliability of these models is often 
questionable. Any change detection based on such models has 
to be aware of this problem and has to accept the incom 
pleteness of the models as well as the inaccuracies of the physi 
cal models or of the simplified implementation used for the si 
mulation. Semi-automatic approaches are reliable alternatives. 
In the following section, the development of the graphics hard 
ware is briefly discussed. In section 3, the real-time SAR simu 
lation tool SARViz will be presented. While in section 4 the 
need for 3D shape awareness in data fusion is discussed. Fusion 
based change detection in urban areas will be demonstrated in 
the final part of the paper. 
Rapid developments in computer graphics allow for more and 
more realistic visualization of extensive virtual worlds in real 
time. Visualization applications are realized by GPUs. A mod 
em GPU is a data-parallel streaming processor working in a 
single-instruction, multiple data (SIMD) fashion. Because 
GPUs are highly parallel and specialized on certain arithmetic 
operations, these operations can be calculated astonishingly fast. 
As depicted in Figure 1, GPUs of the latest generation have 
more computational power as standard CPUs, providing nowa 
days almost supercomputing speed. 
O 150 
-Intel CPU 
2002 2003 2004 2005 2006 
Figure 1. Comparison between CPU and GPU speed (see Buck, 
2004; Owens et al, 2007) 
Beside visualization, the programmable graphics hardware can 
also be used for a variety of general-purpose computations (Ow 
ens et al, 2007). A GPU design differs from a CPU design. As 
shown in Figure 2, a big part of the CPU is used for branching, 
whereas most transistors on the GPU are used for arithmetic op 
erations (Owens, 2005).

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