In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010,1 APRS, Vol. XXXVIII, Part 7B 
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ENHANCING URBAN DIGITAL ELEVATION MODELS USING AUTOMATED 
COMPUTER VISION TECHNIQUES 
B. Sirmacek, P. d’Angelo, T. Krauss, P. Reinartz 
German Aerospace Center (DLR), Remote Sensing Technology Institute 
PO Box 1116, 82230, Wessling, Germany 
(Beril.Sirmacek, Pablo.Angelo, Thomas.Krauss, Peter.Reinartz)@dlr.de 
Commission VII 
KEY WORDS: Urban, Modelling, Detection, DEM/DTM, Cartosat-1 
ABSTRACT: 
In recent years Digital Elevation Models (DEM) gained much interest because of their high capability to give information about urban 
regions. DEM can be used for detailed urban monitoring, change and damage detection purposes. However, initially a DEM with very 
sharp details should be constructed. The DEM can be derived from very high resolution stereo satellite images, but for most of the 
cases just one stereo pair is available. Unfortunately after this process, regions which are occluded in one of the stereo images have 
no height value in the DEM data. This is a major problem especially in urban DEM, since many regions are occluded by buildings. 
However these occluded regions can be filled using interpolation techniques, which lead to lose sharpness in building edges. Besides 
due to low resolution of input stereo images, the generated DEM resolution can be too low to represent buildings. 
In order to increase details, herein we propose a special automated urban DEM enhancement technique. To do so, first we detect 
possible building locations using height information of the DEM. Then using corresponding panchromatic image, we detect building 
shapes with an automatic shape approximation approach. Using detected building shapes, we refine buildings in the DEM. Finally, for 
a better representation we locate constructed three-dimensional building models on Digital Terrain Model (DTM) of the corresponding 
region. We believe that the implemented enhancement will not only provide better three-dimensional urban region representation, but 
also will lead to more detailed change and damage investigation in future studies. 
1 INTRODUCTION 
An important research field in remote sensing is three-dimensional 
analysis and reconstruction of urban objects. Especially urban 
monitoring, damage assessment, and disaster monitoring fields 
need to achieve realistic three-dimensional urban models. A rather 
new technology in this context is the Digital Elevation Model 
(DEM) generation based on stereo image matching principle us 
ing satellite data. Unfortunately, there are several problems in 
generated DEM. First, regions which are occluded in one of the 
stereo images have no height value in DEM data. Interpolation 
techniques, which are used to fill these non-value regions, lead 
to lose sharpness in building edges. Generated DEM have lim 
ited resolution and raw DEM data may not represent buildings 
correctly. In addition to that, DEM does not provide intensity 
and color information. Therefore, some advanced processes are 
required to enhance the DEM. 
In literature, many researchers developed techniques for DEM 
enhancement. A considerable amount of these studies has been 
published on reducing errors in DEM which generally belong to 
rural regions (Skarlatos and Georgopoulos, 2004, Ostrowski and 
He, 1989). In recent years, three-dimensional modeling of urban 
regions gained great interest. Thus, some of the researchers fo 
cused on enhancing urban DEM data for better urban region rep 
resentation. Haala et al. (Haala et al., 1998) proposed a method 
to reconstruct building rooftops using surface normals extracted 
from DEM data. They assumed that building boundaries are 
detected previously. In a following study (Haala and Brenner, 
1999), they detected building boundaries automatically by clas 
sification DEM and corresponding color image before applying 
their automatic rooftop reconstruction method. Brunn and Weid- 
ner (Brunn and Weidner, 1997) used surface normals on DEM to 
discriminate buildings and vegetation. After extracting buildings, 
they measured geometry of rooftops using surface normals and 
they interpolated polyhedral building descriptions to these struc 
tures. Fradkin et al. (Fradkin et al., 1999) proposed segmentation 
based method to reconstruct three-dimensional models of dense 
urban areas. To this end, they used very high resolution color 
aerial images and DEM data. Canu et al. (Canu et al., 1996) used 
high resolution DEM to reconstruct three-dimensional buildings. 
First, they segmented DEM into homogeneous regions. Then, 
they interpolated flat surfaces on these regions. Ortner et al. (Or- 
tner et al., 2002) used point process to model urban areas. They 
represented urban areas as interacting particles where each par 
ticle stands for an urban object. Preknowledge about building 
shapes is used to model these particles. Arefi et al. (Arefi et al., 
2008) extracted above-ground objects from LIDAR data. Then, 
three-dimensional buildings are reconstructed by hierarchical fit 
ting of minimum boundary rectangles (MBR) and RANSAC based 
straight line fitting algorithm. In these studies, good results are 
achieved generally using very high resolution (more than 1 m. 
spatial resolution) DEMs which are generated from airborne data 
sets. However, enhancement of buildings in very low resolu 
tion urban DEM data which is generated from satellite images 
is still an open research problem. As a different approach, Elak- 
sher (Elaksher, 2008) proposed a multi-photo least squares match 
ing based DEM enhancement method. They detected discontinu 
ities in a least squares matching model. Using multiple photos 
of a region, they applied a least squares matching process recur 
sively until the refinement is sufficient. However, the proposed 
method can smooth noises and enhance details in very coarse 
DEM data, it needs multiple photos of the same region taken from 
different looking angles which is generally difficult to obtain, es 
pecially from satellites. 
In another study, Vinson et al. (Vinson et al., 2001, Cohen and 
Vinson, 2002) developed an approach for detecting rectangular 
buildings in DEM. For this purpose, they segmented above ground 
objects in the DEM. Then, they tried to model each above ground