The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BI. Beijing 2008 
of Cartosat-2 multi-view sensors are used for DSM generation 
with very high resolution (1 m) for this study. 
2. IMAGE ORIENTATION 
Generating DEMs from stereo data normally requires the use of 
a geometric model (rigorous physical sensor model) and ground 
control points (GCPs). The collection of GCPs presents a 
significant problem in many practical applications, as an 
existing source of GCPs may not be available. A DEM 
generation method which requires no GCPs would therefore be 
of significant interest to users of stereo data. The RPC model 
was computed for each image and supplied in a text format with 
the Cartosat-2 datasets. 
Rational Polynomial satellite sensor models are simpler 
empirical mathematical models relating image space (line and 
column position) to latitude, longitude, and surface elevation. 
The name Rational Polynomial derives from the fact that the 
model is expressed as the ratio of two cubic polynomial 
expressions. Actually, a single image involves two such rational 
polynomials, one for computing line position and one for the 
column position. The coefficients of these two rational 
polynomials are derived from the satellite’s orbital position, 
orientation and the rigorous physical sensor model. The RPC 
method is a useful method to avoid the development of 3D 
physical models. The RPC method computes the polynomial 
adjustment model for each image [P Cheng, March 2006]. 
AP = Ao + AS x Sample + AL x Line + ASL x Sample x Line + 
(1) 
AR = Bo + BS x Sample + BL x Line + BSL x Sample x Line + 
(2) 
Ao,AS,AL,ASL, and Bo,BS,BL,BSL, are the image 
adjustment parameters, Line and Sample are the line and sample 
coordinates of an image, and AP and AR are the adjustable 
functions expressing the differences between the measured and 
the nominal line and sample coordinates. 
3. DEM GENERATION 
Leica Photogrammetry suite Software has been used for 
modeling and DEM generation. The software supports reading 
of data, manual or automatic GCP/tie points (TP) collection and 
geometric modeling of different satellites including RPC model. 
It is also capable of automatic DEM generation, DEM editing, 
ortho rectification and mosaicking. This RPC method of the 
software is based on the block adjustment method developed by 
Grodecki and Dial. LPS software supports zero order, a first 
order and second order RPC polynomial adjustments. To 
generate DEM a project has been created inside the software. 
The RPC model was computed for each image as per equations 
(1) and (2). Since no ground control point was used (they are 
not available for this area), a zero order polynomial adjustment 
has been considered. A pair of quasi-epipolar image is 
generated from the stereo images to retain elevation parallax in 
the X-direction. An automated image matching procedure is 
then employed to produce the tie points (conjugate points) 
through a comparison of the respective grey values of these 
images. The matching method finds the corresponding pixels in 
the left and right quasi-epipolar images by a hierarchical sub 
pixel mean normalized cross correlation matching method. 
Correlation coefficients are generated between 0 and 1 by this 
matching technique. For each matched pixel, 0 represents a total 
mismatch while 1 represents a perfect match. The points having 
correlation coefficient more than 0.80 have been selected for the 
computation of parallax. The parallax then converted into 
irregular height points which had been converted into regular 
DEM by tin linking and a second order surface fitting. The 
generated Cartosat-2 DEM and the orthoimage of the 
corresponding area are shown in Figure-1. 
The accuracy of the DEM has been checked with SRTM 3-arc 
second DEMs. SRTM is an international project spearheaded by 
National Geospatial Intelligence Agency (NGA) and NASA. 
Since only tie points could be collected between each stereo pair, 
the horizontal positions of the extracted DEM will include 
errors caused by uncorrected biases and errors in the RPCs. 
These errors have been reduced by comparing similar features 
between the extracted DEM and the SRTM DEM, and applying 
offset values in X and Y to the extracted DEM to match the 
SRTM DEM horizontal positions. It has been observed that 
there was a constant shift of 90 m in X and 112 m in Y direction. 
This has been compensated by applying bias in the X and Y 
direction. The Cartosat-2 DEM and the SRTM DEM of same 
area are shown in Figure-2. It can easily be observed that the 
details generated by Cartosat-2 multi-view DEM are much 
better, due to its capability of extraction of finer details of 
elevation from its high resolution data. SRTM DEM was in 
vertical datum EGM 96. So it has been converted into WGS 84 
by applying a bias of 46 m for this area. After applying the shift 
in Longitude and Latitude direction the image difference 
statistics between the two DEMs shows a constant bias of 34 m 
in height. The DEM difference statistics is summarized in 
Table-1. Currently the technique establishes generation of 
relative DEMs in a better grid interval. The capability of this 
technique in terms of the final achievable accuracy is to be 
further assessed with the use of high accuracy GCPs for 
modelling and precision DEM for evaluation. Figure 3 gives 
Cartosat-2 orthoimage drapped over DEM generated from 
muliti-view imagery 
Figure-1 (a): Cartosat-2 DEM sub-sampled at 2 m pixel size 
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