In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
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a bed 
Figure 4. Comparison of SAR and optical viewing geometry under the assumption of locally flat terrain (a); optical data (b) overlaid 
with cadastral building footprint; optical data (e) and LIDAR data (d) overlaid with detected building comer 
from SAR data is set to one third. Such weights are determined 
empirically and lead to good results. However, further research 
has to be done in order to support this choice with reasonable 
statistics. 
A quality measure is assigned to each region and initially set to 
1. In the first evaluation part each primitive is evaluated based 
on the optical feature vector. Each time a feature does not 
completely support the building hypothesis, the quality measure 
is reduced by multiplication with a value between 0 and 1. The 
exact reduction value for each feature was learned on manually 
labelled training data. Such reduced quality measure is again 
multiplied with another reduction value if another feature partly 
rejects a building hypothesis. The final quality measure based 
on the optical feature vector is weighted with 0.666. 
A second region evaluation is conducted based on the comer 
line primitives extracted from InSAR data. First, all building 
object hypotheses are enlarged by two subsequent dilation 
operations. In this manner, a two-pixel wide buffer, 
corresponding to 0.6 meters in ground geometry, is added to the 
original region since building roofs may be shifted away from 
the corner line. Thereafter, it is checked if the corner line 
crosses this enlarged region with a certain minimum length. The 
initial quality measure is multiplied with a reduction value like 
in the optical case if this is not the case. The resulting quality 
measure based on the corner line is multiplied with a weighting 
factor of 0.333. 
Finally, the overall quality measure is obtained by summing up 
the optical and the InSAR quality measures. In case neither an 
optical feature nor an InSAR feature has decreased the quality 
measure, both quality measures sum up to one. All regions that 
have a quality measure greater than an empirically determined 
threshold are classified as building objects. Such threshold was 
set to 0.6. As a consequence, a region may be classified as 
building region even if there is no hint from the InSAR data, but 
strong evidence from the photo. The reason is that some 
buildings do not show corner lines due to an unfavourable 
orientation towards the SAR-sensor (see the gabled-roofed 
buildings in the lower right corner of Fig. 5b) or occlusion of 
the potential corner line region by plants. On the contrary, a 
region cannot be evaluated as building region based merely on 
the corner line which are strong hints for buildings but may also 
caused by other abrupt height changes in urban areas. 
an effective baseline of approximately 2.4 m. The mapped 
residential area in the city of Dorsten in Germany is 
characterized by a mixture of flat-roofed and gable-roofed 
buildings and low terrain undulation. 
Results of the presented approach for building recognition by 
means of feature combination from optical imagery and InSAR 
data are shown in Fig. 5. In Fig. 5a building recognition results 
based solely on optical features are displayed. All parameters 
where specifically adjusted in order to achieve the lowest 
possible false alarm rate while still detecting buildings. Less 
than 50% of the buildings contained in the displayed scene are 
detected. In addition, false alarms could not be avoided 
completely. Results are rather poor due to the assumption that 
roofs do not split up into more than two regions during the 
region growing step, which is not met for the data at hand. As a 
consequence, several gable-roofed buildings with reddish roofs 
in the lower right corner of the image could not be recognized. 
Some big flat-roofed buildings in the upper part of the image 
are not detected because their colour and shape are similar to 
such of street segments. Thus, their evaluation value does not 
exceed the threshold. 
Fig. 5b shows the corner lines extracted from the InSAR data 
superimposed onto one SAR magnitude image. An InSAR 
corner line could be detected for almost all buildings in this 
scene. Some lines are split into two parts because the 
corresponding building was partly occluded by, e.g., plants. 
Some corner lines in the lower right diagonally cross buildings 
which is not plausible. Most likely this effect is an artefact 
introduced by too large tolerances applied in the merging and 
prolongation steps of adjacent line segments. The final building 
recognition result using both optical and InSAR features is 
shown in Fig. 5c. The overall building recognition rate could be 
significantly improved to approximately 80% by integration of 
the InSAR corner lines into the classification procedure. 
Additionally, all false alarms could be suppressed. However, the 
gable-roofed buildings in the lower right comer stay undetected 
although InSAR corner lines are present. Such missed 
detections are due to the over-segmentation of the rather 
inhomogeneous roof regions in the optical image. 
6. CONCLUSION AND OUTLOOK 
5. RESULTS 
The InSAR data used in this project was recorded by the AeS-1 
sensor of Intermap Technologies. The spatial resolution in 
range is about 38 cm while 16 cm resolution is achieved in 
azimuth direction. The two X-Band sensors were operated with 
In this work, first building detection results from combined 
optical and InSAR data on feature level were presented. A 
rather simple approach for feature fusion was introduced 
leading to a significantly improved building recognition rate. 
Additionally, the number of false alarms could be reduced 
considerably by the joint use of optical and InSAR features. 
Corner lines from InSAR data proved to be essential hints for