107 
In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Voi. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
should be controlled manually in order to reach a correctness of 
95%. 
Possible assignment 
Result 
Correct assignment 
76.99% 
Manual control necessary 
17.87% 
Possibly correct assignment 
4.96% 
Wrong assignment 
0.18% 
Table 1 : Results Scenario: Elbe 
The results are obtained with the threshold parameters t\ =0.5 
and ¿2 = 0.001. The variations of the parameters are depicted 
in Figure 7. The parameters are responsible for the amount of 
road segments which are assigned to the state possibly flooded on 
condition that they are classified to the classes water or road. The 
decrease of’’Wrong assignment” comes along with the decrease 
of’’Correct assignments” and an increase of manual control. 
Prameter t 1 = 0.5 Prameter ^ = 0.001 
Figure 7: Results dependent on parameter t\ and 12 (red = Wrong 
assignment, orange = Possibly correct assignment, yellow = Man 
ual control necessary, green = Correct assignment) 
P*Jg) 
Figure 8: Combination of probabilities and impact of the param 
eter ti 
In Figure 8 the combination of the probabilities p / (a) and p lJJw (g) 
is shown. The grayscale bar indicates the combined probability 
Pf(g, a). Every star defines a road segment assigned to the class 
water by multispectral classification, the color shows the state as 
signed in the reference. Many road segments which are assigned 
to the state trafficable in the reference are wrongly classified by 
the system to the class water. The reason is the high standard 
deviation of the probability densitiy function for the class road 
and, therefore, the overlapping of the class road and water. Road 
segments in urban areas occluded by shadows are responsible 
for this effect. The threshold t\ is depicted in blue which dev- 
ide the assignment of the roads to the state flooded and possible 
flooded (Figure 8). Shifting this parameter leads to the results il 
lustrated on the right plot in Figure 7. Furthermore, the improve 
ment of the combined probability is shown in Figure 8. If only 
one probability is available, the threshold t\ would be depicted 
as a straight horizontal or vertical line. The total required time 
to generate the manual reference is about three hours. Compared 
to the time needed for the automatic classification (less than one 
minute) points out the efficiency of the approach. 
The results of the second test scenario are depicted in Figure 9. 
A detail of the original TerraSAR-X scene and the assessed road 
segments is shown in Figure 10. 
Figure 9: Automatic assessment of roads using the classification 
system: flooded roads (red), trafficable roads (green) and possibly 
flooded roads (yellow) 
Figure 10: Detail of original and assessed TerraSAR-X scene 
In the second test scenario the real ground truth is available. Hence, 
the assignment possibly flooded is not existing in the reference 
data. The comparison with the automatic classification system 
leads to the result shown in Table 2. After controlling 5% man 
ually, altogether over 86% are correctly assigned. The value 
of 14% of wrong assignment is caused by mainly two reasons: 
Firstly, the resolution of the StripMap mode hardly enables to