Figure 2: DSM, DTM and nDSM. 
2.3 Building Detection 
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. 
By applying the Hough Transformation 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, 2004). 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 Vozikis (2004) and Gonzalez and Woods 
(1992).Briefly it can be described as follows: 
p = x cos(0) - y sin(<9) 
(1) 
where p is the perpendicular distance of a line from the origin 
and 0 the angle (in the range 0 to rc) as illustrated in Figure 4. 
To apply this function on the whole image, Equation 1 can be 
extended as shown in Equation 2 (IDL, 2004).