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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
A NOVEL 3D CITY MODELLING APPROACH FOR SATELLITE STEREO DATA USING
3D ACTIVE SHAPE MODELS ON DSMS
Beril Sirmacek, Hannes Taubenboeck, Peter Reinartz
German Aerospace Center (DLR), Earth Observation Center (EOC)
PO Box 1116, 82234, Wessling, Germany
(Beril.Sirmacek, Hannes.Taubenboeck, Peter.Reinartz)@dir.de
Commission III/4
KEY WORDS: Building, Modelling, City, DBM/DTM
ABSTRACT:
Since remote sensing provides new sensors and techniques to accumulate stereo data on urban regions, three-dimensional (3D) repre-
sentation of these regions gained much interest for various applications. 3D urban region representation can e.g. be used for detailed
urban monitoring, change and damage detection purposes. In order to obtain 3D representation, one of the easiest and cheapest way is
to use Digital Surface Models (DSMs) which are generated from very high resolution stereo satellite images using stereovision tech-
niques. Unfortunately after applying the DSM generation process, we cannot directly obtain a full 3D urban region representation. In
the DSM which is generated using only one stereo image pair, generally noise, matching errors, and uncertainties on building wall loca-
tions are very high. These undesirable effects prevents a DSM to provide a realistic 3D city representation. Therefore, some automatic
techniques should be applied to obtain real 3D city models using DSMs as input. In order to bring a solution to the existing problems
in this field, herein we propose a fully automated approach based on the usage of a novel 3D active shape model. Our experimental
results on DSMs of Munich city which are obtained from different satellite (Cartosat-1, Ikonos, WorldView-2) and airborne sensors
(3K camera, HRSC, and LIDAR) indicate possible usage of the algorithm to obtain 3D city representation results automatically.
1 INTRODUCTION
As satellite and airborne sensor technology provides higher imag-
ing qualities, especially 3D representation of cities gained much
interest for various applications. For obtaining 3D representa-
tion, Digital Surface Models (DSMs) can be generated from opti-
cal stereo satellite or aerial images using stereovision techniques,
or they can also be obtained by using LIDAR sensor technol-
ogy. As a challenge, for satellite data, in most of the cases, just
one stereo image pair is available for DSM generation. Unfortu-
nately, after applying an automatic DSM generation process, due
to the occlusion effects and stereo matching errors these DSMs do
not correctly represent 3D city models with steep building walls
and detailed rooftop representations. This is a major problem es-
pecially for DSMs which are generated over city centers, since
many regions are occluded by dense and complex building struc-
tures. Although these occluded regions can be filled by interpola-
tion, these techniques lead to a decrease in sharpness of building
walls. Besides, deficiencies in the stereo matching process may
cause noise within DSMs, e.g. due to shadow areas. Therefore,
automatically obtaining 3D city models from DSMs is still an
open and challenging problem for researchers.
In the previous work there is a wide variety of studies on building
detection and shape extraction from two-dimensional single satel-
lite or aerial images. The earliest studies in this field generally
depend on edge and line extraction (Krishnamachari and Chel-
lappa, 1996, Irvin and McKeown, 1989, Davis, 1982). Unfortu-
nately, these methods generally fail to detect individual buildings
Which have highly textured rooftops or which appear in com-
plex environments. In order to cope with this problem, Saeedi
and Zwick (Saeedi and Zwick, 2008) combined edge informa-
tion with graph based segmentation results of the region. Many
researchers developed more advanced methods to extract shapes
of the detected buildings (Karantzalos and Paragios, 2009, Cui et
al., 2008, Benedek et al., 2009). Sirmacek and Unsalan (Sirma-
cek and Unsalan, 2010) developed a fast method to detect shapes
of rectangular buildings which depends on growing a rectangular
active shape. Unfortunately, they could not detect other build-
ing shapes with their approach. With the upcoming availabil-
ity of DSMs from optical stereo images and from light detec-
tion and ranging measurements (LIDAR), many researchers be-
gan to pay attention to building detection from these data. Wurm
et al. (Wurm et al., 2011), extracted 3D block models using an
object-oriented approach based on data fusion from LIDAR and
VHR optical imageries. Rottensteiner et al. (Rottensteiner et
al., 2007) applied the DempsterShafer fusion of airborne laser
scanner (ALS) point clouds and multispectral images for build-
ing detection. Haala et al. (Haala and Brenner, 1999) proposed
a method to reconstruct building rooftops using surface normals
extracted from LIDAR DSM data. They assumed that building
boundaries are detected previously. In a following study (Haala
et al., 1998), they detected building boundaries automatically by
classifying a DSM and corresponding color image before ap-
plying their automatic rooftop reconstruction method. Brenner
et al. (Brenner et al., 2001) discussed rooftop type derivation
methods using given groundfloor shapes. They also projected
two-dimensional terrestrial building facade images to generated
models and obtain realistic 3D city representations. Brunn and
Weidner (Brunn and Weidner, 1997) used surface normals on a
DSM to discriminate buildings and vegetation. After detecting
buildings, they measured the geometry of rooftops using surface
normals and they interpolated polyhedral building descriptions to
these structures. Fradkin et al. (Fradkin et al., 1999) proposed
a segmentation based method to reconstruct 3D models of dense
urban areas. To this end, they used very high resolution color
aerial images and disparity maps. Canu et al. (Canu et al., 1996)
used a high resolution DSM, which is obtained by stereo match-
ing techniques, in order to reconstruct 3D buildings. First, they
segmented the DSM into homogeneous regions. Then, they in-
terpolated flat surfaces on these regions. Ortner et al. (Ortner
et al., 2002) used a point process (Jacobsen, 2005) to model ur-
ban areas. They represented urban areas as interacting particles
