CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
2.1.1 Pre-Processing 
This step comprises the procedures from orientation of the input 
data up to the DSM (Digital Surface Model) creation. For VHR 
satellite imagery the orientation approach is based on the RFM 
(Rational Function Model) (Vozikis et al., 2003). When dealing 
with aerial imagery it is made use of GPS/INS information in 
order perform direct georeferencing, and thus automated image 
triangulation (Scholten and Gwinner, 2003). The DSM 
extraction is performed by automated correlation procedures, 
which nowadays are very mature and produce very good 
results. 
2.1.2 nDSM Creation 
The goal is to derive the DTM (Digital Terrain Model) from the 
DSM and subtract it from the DSM in order to produce the so- 
called nDSM (normalized Digital Surface Model). This way all 
extruding objects in the data set (including buildings) stand on 
elevation height 0 (Figure 2). 
Figure 2: DSM, DTM and nDSM. 
2.1.3 Building Detection (Seeding) 
This crucial step deals with the identification of potential 
building candidates in the data sets (=determination of seed 
points inside buildings). It is proposed to perform 2 statistical 
analyses. First, perform a thresholding in the nDSM and filter 
out all objects that are not taller than a certain height, and 
second, perform texture analysis in the image data to keep only 
roof-similar regions in the data set (Vozikis, 2004). 
Figure 3: Computation of seed points (red asterisks) inside 
potential building candidates by height-thresholding 
and texture filtering. 
2.1.4 Building Extraction 
By applying the Hough Transformation (to an image of gradient 
or of contours) the geometric properties of the buildings 
(building edges and comers) are extracted. Our approach is 
based on a stepwise, iterative Hough Transformation in 
combination with an adaptive region growing algorithm 
(Vozikis and Jansa, 2008). The general idea is to transform the 
information in the image (feature space) into a parameter space 
and apply there an analysis. It is a technique for isolating 
features that share common characteristics. The classical Hough 
transformation is used to detect lines, circles, ellipses etc., 
whereas the generalized form can be used to detect features that 
cannot easily be described in an analytical way. 
The mathematical analysis of the Hough Transformation is 
described in detail in Gonzalez and Woods (1992). 
Briefly it can be described as follows: 
p = xcos(#)~ vsin(#) (1) 
where p is the perpendicular distance of a line from the origin 
and 0 the angle (in the range 0 to 7r) as illustrated in Figure 4. 
To apply this function on the whole image, Equation 1 can be 
extended as shown in Equation 2. 
H(e,p)= \ jF(x,y)S(p- xcos(#)-ysin(#))<ir£fr (2) 
where 8 is the Dirac delta-function. Each point (x,y) in the 
original image F(x,y) is transformed into a sinusoid p = xcos(0) 
- ysin(0). 
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