In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
542 
segment by a rectangle. They used estimated rectangular shapes 
to enhance building representation in DEM. Herein, we provide 
a fast and fully automatic approach to enhance DEM data based 
on building shape approximation. For this purpose, first we ex 
tract above ground objects in DEM. Since our DEM data are in 
very low resolution (in 5 m. spatial resolution), it is not conve 
nient to extract building shapes. Therefore different from Vinson 
et al., we detect building shapes from panchromatic image of re 
gion. To detect building shapes, we benefit from automatic rect 
angular shape approximation approach (Box-Fitting) (Sirmacek 
and Unsalan, 2008). Finally, using detected building shapes we 
refine the DEM data. For a better representation, we also recon 
struct building shapes on interpolated and smoothed Digital Ter 
rain Model (DTM) of the corresponding region. The resulting en 
hanced three-dimensional data will not only provide better visual 
result, but also will provide a basis for detailed three-dimensional 
modeling and change detection analysis. 
2 DETECTING BUILDING LOCATIONS USING DEM 
AND DISTANCE TRANSFORM 
In a previous study, d’Angelo et al. proposed a fully automated 
method to generate DEM (d’Angelo et al., 2009). For this pur 
pose, they applied hierarchical intensity based matching, and then 
dense epipolar matching to stereo images with 2.5 m. ground 
sampling distance (GSD) taken from the Cartosat-1 satellite. Look 
ing angle differences of two satellite images are about 31°, which 
is too much higher than normally wanted to reconstruct build 
ings. Normally, 10° angle difference between stereo image pairs 
is wanted to reconstruct buildings. Unfortunately, it is very diffi 
cult to obtain stereo image couples with this small looking angle 
from satellite images. In another study, Arefi and Hahn (Arefi 
and Hahn, 2005) proposed a DTM generation method from LI- 
DAR based on geodesic dilation. Then, Arefi et al. (Arefi et al., 
2009) developed the algorithm for DTM generation from DEM. 
Herein, we use DEM and DTM data which are generated using 
methods of d’Angelo et al. and Arefi et al. which are reported 
in (d’Angelo et al., 2009) and (Arefi et al., 2009) respectively. 
The difference between DEM and DTM is known as normal 
ized Digital Elevation Model (nDEM). In the normalized DEM 
ground height is referenced to zero, therefore it provides informa 
tion about approximate building heights independent from the ter 
rain. To estimate urban areas, we first generate nDEM (N(x, y)) 
by taking difference of DEM (E(x, y)) and DTM (T(x, y)) im 
age matrices which belong to the same region. Then, we ap 
ply Otsu’s automatic thresholding method to detect urban area 
in N(x,y) (Otsu, 1979). After applying thresholding, we as 
sume output M(x,y) binary image as urban area mask which 
holds K number of binary subregions. In order to eliminate ef 
fect of trees, we analyze each Mk(x, y) k E [1,2,..., K] subre 
gion in M(x, y) urban area mask. If max(A r (x, y) x Mk{x, y)) 
is smaller than 2 meters, we eliminate Mk(x, y) subregion since 
it is not high enough to represent a building. In Fig. 1(a), we 
represent Jeddai test image from our data set, and in Fig. 1(b) 
we show detected urban area boundaries. 
After finding the urban area from the DEM, we label buildings in 
order to model each of them with a rectangular shape. Unfortu 
nately, due to very low resolution of this DEM and high complex 
ity of the region, it is not possible to always distinguish adjacent 
buildings from DEM data. Therefore we pick panchromatic im 
age of region (I(x, y)) for further analysis. First, we apply a pre- 
process to I(x, y) image using bilateral filter which performs a 
non-linear smoothing with preserving edge information (Tomasi 
and Manduci, 1998). In this way, we eliminate noise and redun 
dant details in image. Sirmacek and Unsalan provides an exten 
sive explanation about usage of bilateral filter in panchromatic 
satellite images (Sirmacek and Unsalan, 2009). To find build 
ings, we benefit from Canny edges (Canny, 1986). We extract 
Canny edges of I(x, y) test image, then we use M(x, y) urban 
area mask to obtain only building edges. For our Jeddai sam 
ple test image, detected building edges can be seen in Fig. 1(c). 
Then, we use distance transform to find a location inside of each 
closed building edge shape. For binary images (like our building 
edges in Fig. 1(c)), distance transform calculates the distance be 
tween each pixel that is set to zero and the nearest nonzero pixel. 
In our study, we use Euclidean Distance as distance metric. Af 
ter applying distance transform to our building edges, centers of 
building shapes get highest values. Consequently, we pick local 
maximum values in distance transform, and assume their loca 
tions (Xb,yb) as possible building centers. In Fig. 1(d) we rep 
resent detected building locations for our Jeddai test image. As 
can be seen in this figure, most of the buildings are labeled cor 
rectly. Next, we describe the proposed automatic building shape 
approximation method. 
(c) (d) 
Figure 1: (a) Jeddai test image, (b) Detected urban area bound 
aries. (c) Canny edges detected in urban area, (d) Possible build 
ing centers (x b ,y b ). 
3 EXTRACTING BUILDING SHAPES (BOX-FITTING) 
In complex urban areas which contain adjacent buildings, very 
low resolution DEM data can not be used for detecting shapes of 
buildings. Therefore, we use Canny edges which are extracted 
from panchromatic image to estimate building shapes. In a pre 
vious study Sirmacek and Unsalan (Sirmacek and Unsalan, 2008) 
proposed an automatic shape approximation approach (called Box- 
Fitting) after a seed-point is detected on the building rooftop. In 
this study, we benefit from this Box-Fitting approach to detect ap 
proximate building shapes. We assume (Xb, yb) possible building 
centers as seed-points to run Box-Fitting algorithm. 
To estimate building shapes for each (Xb, yb) location, we locate 
a [w x to] size window on this building center. Considering res 
olution and approximate building sizes in our test images, we as 
sume w as equal to 60 pixel. Box-Fitting method discards edges