The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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3.2 Automatic extraction: Requirements through 
characteristics of available sensors and images 
To enable a fast reaction after a disaster, special emphasis 
should be put on the exploitation of multi-sensorial optical and 
SAR satellite images, as the current and in particular the future 
availability of these sensors allows to acquiring such images 
nearly everywhere and at any time. Optical images show strong 
advantages concerning the radiometric image quality if they 
were taken during good weather and illumination conditions. 
On the other hand, SAR holds some prominent advantages over 
optical images, which are in particular helpful in crisis 
situations. SAR is an active system that can operate during day 
and night. It is also nearly weather-independent and, moreover, 
during bad weather conditions, SAR is today’s only operational 
system available at all. However, as coherent imaging 
technique, SAR images are affected by the well-known speckle 
noise as well as image derogations due to radar shadow and 
layover. The imaged objects are thus subject to drastic changes 
in their appearance depending on the radar illumination 
parameters. The challenges of man-made object extraction from 
SAR data, in particular roads and buildings, can be viewed in 
(Wessel & Hinz 2004, Sorgel et al. 2006). The need for and the 
potential of further developments in this area has been 
recognized years ago, but it will be even more important in the 
future as consequence of the high potential of new airborne and 
spaceborne sensor systems such as TerraSAR-X, TanDEM-X, 
or Radarsat-2. 
Driven by the technological advances, recent work in the field 
of automatic object extraction shows the importance of 
developing models and strategies that combine evidence from 
various sources in a sound statistical framework. The evidence 
may stem, e.g., from multiple views, different sensors, or 
external data. The approach of (Wessel & Hinz, 2004; Wessel, 
2006; Hedman et al., 2006, 2007, 2008) may serve as example 
for road extraction from single and multiple SAR images. It 
models the sensor- and context-dependent appearance of roads 
and is meanwhile adapted to Bayesian decision theory in order 
to consistently incorporate multiple SAR images and knowledge 
about the appearances and relations of objects. The first step 
consists of a line extraction in each image, followed by attribute 
extraction. Based on these attributes the uncertainty of each line 
segment is estimated statistically, followed by an iterative 
fusion of these uncertainties supported by context information 
and sensor geometry (see Fig. 1). On the basis of a resulting 
uncertainty vector each line obtains an estimation of the 
probability that the line really belongs to a road. The final step 
includes the generation of a road network by calculating optimal 
paths through the weighted graph of line segments and 
connections between them. Figure 2 shows the result of 
employing context information about forest and urban areas into 
the extraction of roads. The borders of these areas serve as seed 
points for generating the road network, while the interior of 
these areas is excluded. An evaluation with manually digitized 
reference has shown that a completeness of approx. 70% can be 
reached when accepting a correctness also of approx. 70%. 
Figure 1. Bayesian fusion module and its input data 
As expected, both correctness and completeness increase when 
applying a similar version of this extraction system to optical 
images. (The only difference between the two versions is the 
procedure for primitive extraction and evaluation.) Figure 3 
visualizes the result of road extraction on an optical image of 
similar resolution and similar scene complexity. Here, a 
completeness of approx. 85% is reached while the correctness is 
still over 90%. The example shown later in Fig. 6 illustrates, 
though, that even a low percentages of cloud coverage may 
influence the quality of the results. We refer the interested 
reader to (Mayer et al. 2006), where more details and a 
thorough comparison and discussion of different automatic road 
extraction approaches applied to optical images can be found. 
Figure 2. Result of road extraction from SAR X-/L- pair employing global context (from (Wessel 2006)). White lines: extracted roads; black lines: 
missing roads; dotted lines: context area “urban” (seed point for extraction); black areas: context area “forest”.