(IX-B3, 2012 
    
  
  
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 SEMIAUTOMATIC APPROACH FOR GENERATION OF SITE MODELS FROM 
CARTOSAT-2 MULTIVIEW IMAGES 
Archana Mahapatra, Sumit Pandey, D Sudheer Reddy, P S Subramanyam, B. K. Das, P V Radhadevi, J Saibaba, Geeta Varadan 
Advance Data Processing Research Institute, Department of Space, 
203 Akbar Road, Secunderabad-500009, INDIA, 
archaana(Dadrin.res.m 
Commission III, WG II[/4, III/5 
KEY WORDS: Site Model, Physical Sensor Model, Relative orientation, conjugate points, edge extraction, normalized DSM 
ABSTRACT: 
In the last decade there has been a paradigm shift in creating, viewing and utilizing geospatial data for planning, navigation and 
traffic management of urban areas. Realistic, three-dimensional information is preferred over conventional two dimensional maps. 
The paper describes objectives, methodology and results of an operational system being developed for generation of site model from 
Cartosat-2 multiview images. The system is designed to work in operational mode with varying level of manual interactivity. À 
rigorous physical sensor model based on collinearity condition models the ‘step n stare’ mode of image acquisition of the satellite. 
The relative orientation of the overlapping images is achieved using coplanarity condition and conjugate points. A procedure is 
developed to perform digitization in mono and stereo modes. A technique for refining manually digitized boundaries is developed. 
The conjugate points are generated by establishing a correspondence between the points obtained on refined edges to analogous 
points on the images obtained with view angles +26 deg. It is achieved through geometrically constrained image matching method. 
The results are shown for a portion of multi-view images of Washington City obtained from Cartosat-2. The scheme is generic to 
accept very high resolution stereo images from other satellites as input. 
1. INTRODUCTION 
A site model represents both the natural and man-made features 
such as terrain, road, and buildings to sufficient level of detail 
so that it can be considered as a true model of the area under 
consideration. Buildings are important objects of any 3D city 
model. The algorithms for fully automatic extraction of 
buildings are not matured enough as a result many researchers 
in this field opt for semi automatic methods (Gruen 99). 
Building extraction requires representation of roof structures. A 
finer model represents roof details, overhangs and includes 
realistic texture of the building (Ulm and Poli, 2006). It can also 
have architectural details of the building. In a coarse model, 
detection of the buildings is only possible. A DSM rendered 
with texture may be considered as coarse city model (Kraub, 
2008). 
The inputs required for generating finer details includes aerial 
images, terrestrial images, LIDAR measurements and building 
plans (Vosselman et al, 2001). The geometric resolution and 
radiometric quality of the images are important as it should be 
possible to identify and delineate features. 
Aerial images have the advantage of very high geometric 
resolution and low noise levels. In the last few years, there has 
been considerable improvement in the technology to provide 
better resolution images from space platforms. The images from 
IKONOS, Quickbird, Worldview-1/2, Geoeye-1, and Cartosat-2 
have geometric resolution less than one meter. These geometric 
resolution levels are not sufficient to generate finer models, 
however extraction and representation of major features are still 
possible. 
The objective of this work is to develop a semiautomatic system 
to generate site models from satellite stereo images/multiview 
Images in operational mode. At present the focus is mainly on 
the extraction and representation of building flat roof tops. It is 
not envisaged to generate a photorealistic site model, 
reconstruction of complex building roof structures and 
individual representation of trees. 
The paper describes in brief the imaging geometry of Cartosat-2 
and physical sensor model developed, followed by details of 
computation of relative orientation parameters and comparative 
analysis of rational function model with physical sensor model. 
A method of refining digitized edges with the help of canny 
edges is briefed. The semiautomatic mode of data capture and 
image matching techniques are described subsequently. A 
section is devoted to explain generation of DSM and DTM. 
Finally results are presented for a portion of the Washington 
city. 
2. METHODOLOGY 
2.1 Cartosat-2 Imaging Geometry 
India’s highest resolution imaging satellite, Cartosat-2, was 
launched in 2007. The spatial resolution of better than one 
meter in panchromatic band is achieved through ‘step n stare’ 
mechanism of image acquisition. In this image acquisition 
process, the effective ground velocity is reduced by 
continuously steering camera in the direction opposite to 
spacecraft motion. Reduction of ground trace velocity in turn 
improves the spatial resolution of the image in along-track 
direction. To reduce the pixel cross talk and improve the image 
quality, the CCD arrays have staggered configuration in the 
focal plane. Even and odd pixels of a line are recorded by the 
two detector arrays that are separated by 351 in the focal plane. 
It means that the adjacent pixels of a line are not imaged at 
same instant. If all odd numbered pixels of a line are imaged at 
instant t, the even numbered pixels will be imaged at instant 
represented by t + 5xintegration time.