The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
map contains terrain, forest, vegetation, roads, lakes, buildings, 
and geographic names. Synthesizing the concepts associated 
with photogrammetry, remote sensing, GIS, and 3D 
visualization introduces a new paradigm for the future of digital 
mapping. Correctly combining height information with existing 
2D maps has a great potential for a fast, accurate and highly 
automated generation of maps [2]. 
This paper describes a methodology to update 1:25000 
topographical map using Cartosat-1 and Cartosat-2 images in an 
integrated approach. The other objectives are to test the 
capability of feature extraction in stereo mode from the 
Cartosat-1 stereo data and comparison between features directly 
traced from Cartosat-2 ortho-image. This approach include data 
pre-processing, modeling of space imagery, DEM and Ortho 
image generation with or without GCPs, accuracy improvement 
of DEM and Ortho-image, Image to Image registration, 2D and 
3D feature extraction and mapping in a standard cartographic 
environment. This study also provides a guideline for the 2D or 
3D extraction for different feature types. 
2.0 STUDY AREA 
Covering an area of 7.5’X7.5’ of the test site is bound between 
geo-coordinates 17° 22’30” N to 17° 30’ N and 78° 22’30” E to 
78° 30’ E. The study area forms part of Hyderabad City and the 
general elevation of the area ranges from 400 m to 630 m above 
mean sea level. 
3.0 INPUT DATASETS 
Cartosat-1 stereo images and Cartosat-2 datasets have been 
acquired for the study area. Both the satellite datasets were 
accompanied by respective RPC files. The verification was 
made for the datasets after receiving the datasets. There were 20 
Ground Control Points used for this project for modeling and 
accuracy evaluation. 
4.0 METHODOLOGY 
A systematic framework of the algorithm used to generate 
digital maps is presented in figure-1. The analysis of 
methodology can be grouped into three stages. The first stage 
incorporates the Cartosat-1 processing for DEM generation and 
3D feature extraction. The second stage related to Cartosat-2 
processing and feature extraction in 2D. The final stage used a 
set of overlay decision rules to inject a knowledge-based feature 
comparison based on suitable features for mapping from the 
two satellites. 
A project for DEM generation and stereo processing is created 
in Lieca Photogrammetry Suite (LPS) S/W and the Cartosat-1 
stereo images were imported. The interior orientation has been 
computed for both fore and aft camera images (BandA & 
BandF) using RPC. 15 GCPs have been identified on both the 
images for Exterior orientation computation. Image matching 
has been performed between the fore and aft images. A total 
number of 6326 match points called as Tie points have been 
generated at more than 0.80 correlation coefficients. 
Triangulation has been performed with RPC at 2 nd order 
polynomial refinement. The regular DEM has been generated at 
10 m grid interval. Finally Ortho-image is generated and 
features have been extracted in 2D mode by on screen 
digitization. The same model has also been used for 3D feature 
extraction. All the man made structures (buildings, fence etc.) 
and Trees have been captured in 3D environment in stereo 
mode. The accuracy achieved for DEM and orthoimage is better 
than 5 m. 
The Cartosat-2 image has been registered with Cartosat-1 ortho 
image using the Automatic Point Measurement (APM) 
Software tool [3]. APM uses image matching technology to 
automatically recognize and measure the corresponding image 
points between the two images. APM deliver the coordinates of 
evenly distributed corresponding points between an input image 
and a reference image. The search and cross-correlation 
window sizes for matching were set to 17 and 11 respectively. 
The window size for least square matching for this project has 
been taken as 10 pixels. The limit for cross correlation 
coefficient was 0.80. A total of 500 well distributed points 
between the images have been generated at an RMS Of 0.42 
pixels with a standard deviation of 0.25. An image to image 
affine transformation was performed to register the Cartosat-2 
image with respect to Cartosat-1 image. Final resampled 
Cartosat-2 image has also been visually compared with 
Cartosat-1 orthoimage. List of features captured in 2D and 3D 
mode from Cartosat-1 and Cartosat-2 ortho-images is given in 
table-1. 
Figure 1 : Mapping schematic work-flow frame work 
5.0 RESULTS AND CONCLUSIONS 
A comparative study has been made for the features extracted in 
2D from Cartosat-1 and Cartosat-2 ortho-images and it has been 
observed that feature extracted from Cartosat-2 is giving better 
result with respect to features shape and size. This is obvious 
due to the higher resolution of Cartosat-2 with respect to 
Cartosat-1. The resolution difference has been shown in figure 
1(a) and 1(b). It has been observed that capturing features in the 
city like Hyderabad using Cartosat-1 in 2D is very difficult. 
One can only capture the building blocks from Cartosat-1 
[figure-2] which depends on the density of buildings. But 
buildings can be captured in stereo mode in 3D [figure-3] with 
Cartosat-1 data, which nearly match with those derived from 
Cartosat-2 in 2D mode.