CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
a b c 
Figure 5. Results of building detection based on optical data (a), detected corner lines in the InSAR data (b), and of building 
detection based on InSAR and optical data (c) 
buildings. Such comer lines also appear in single SAR images 
and hence this approach is not limited to InSAR data. 
Further developed, this approach may be the basis for a change 
detection method after natural hazards like flooding and 
hurricanes. An optical image acquired before the hazard and 
SAR data acquired afterwards can be analyzed using the 
presented approach. A human interpreter would only have to 
check those buildings for damages that were not detected from 
both data sources. Hence, all buildings recognized from the 
combination of optical and SAR features, shown in red in Fig. 
5c, would be classified as undamaged. Only buildings in the 
optical image that where not detected would have to be checked 
speeding up the entire damage assessment step significantly. 
Although first results are encouraging, further improvements 
have to be made. One main disadvantage of the presented 
classification approach is that its quality measures are not 
interpretable as probabilities in a Bayesian sense. Although 
many parameters have been learned from training data, parts of 
the approach are still ad-hoc. A next step will thus be the 
integration of the presented approach into a Bayesian 
framework. 
Furthermore, the differences of the sensor geometries should be 
used for further building recognition enhancement. Since the 
roofs of high buildings are displaced away from the sensor and 
parts of the façade appear in the image, roof regions have to be 
shifted towards the sensor in order to delineate building 
footprints. Such displacement also bears height information 
which may be used as an additional feature for building 
recognition. More height information may also be derived 
directly from the InSAR data. 
Finally, three-dimensional modelling of the scene could be 
accomplished based on the building footprints, a height 
hypothesis and maybe even the estimation of the roof type. An 
iterative joint classification and three-dimensional modelling in 
a Bayesian framework, including context information, will be 
the final goal of this project. 
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