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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
The Data Products(DP) team at Space Applications Centre, 
Ahmedabad has established operationalisation of DP s/w and 
Stereo Strip Triangulation(SST) s/w at ground processing 
facility in India. While DP s/w caters to generation of good 
quality orthokit, ortho products to national/global users (Nanda 
Kumar et al. 2005), SST s/w is meant for generation and 
archival of strip Digital Elevation Model(DEM) and 
Triangulated Control Points(TCPs) from stereo strips of 
Cartosat-1 for generating highly accurate data products. Using 
the above two s/w along with additional utilities, Cartosat DP 
team had conducted study and specific exercises related to in 
flight calibration of Cartosat-1 and Cartosat-2 for modelling 
imaging geometry during initial phase of operations to assess 
and qualify the mission performance. This becomes essential 
and important activity for any remote sensing mission before 
declaring it operational for normal use. Significant 
improvements in the location accuracy and internal distortion 
of Cartosat data products have been achieved after 
incorporating various interior and exterior orientation 
parameters determined from the imagery. Similar exercises 
were carried out for Cartosat-2 during January 2007 to April 
2007. 
This paper describes the methodology and experimental details 
of in-orbit exercises carried out during the initial phase of 
operations by which the imaging geometry of both Cartosat-1 
and Cartosat-2 cameras was re-established. Also, details on the 
development of new approach using stereo imaging sensors 
with minimum or no control for Cartosat-1 are addressed. 
Results and discussions on in-flight geometric calibration 
experiences for Cartosat-1 and Cartosat-2 are presented in this 
paper. 
2. IN-FLIGHT CALIBRATION 
One of the important activities during post-launch period of 
mission qualification stage is to assess the mission performance 
in terms of geometric quality and improve further using in 
flight calibration exercises. As mentioned earlier, the geometric 
accuracy of data products for Cartosat-1 and Cartosat-2 is 
determined by the knowledge of precise imaging geometry as 
well as the capability of the imaging model to use this 
information. Though the system level knowledge of various 
parameters contributing to the imaging geometry (e.g. 
alignment angles between spacecraft cube normal to payload, 
star sensor to payload, inter sensor alignment angles, orbit, 
attitude, rate parameters) are used in the geometric correction 
process with an a-priori knowledge, all in-flight parameters 
(both camera and platform) are well characterized and re 
established with the real data from the respective sensors after 
the launch. 
In-flight calibration is required (i) to achieve the specified 
system level accuracy of the data products consistently through 
out the mission life (ii) to obtain the precise relation between 
the data products of various sensors, so that data 
fusion/merging of these data sets becomes more easier for 
further applications, (iii) for better mosaicking of data between 
the scenes/strips, (iv) for generating precise DEMs (Cartosat-1 
stereo pairs), (v) for generation of precision products and (vi) 
for understanding and improving the system performance and 
(vi) for validating various payload and mission parameters. In 
flight calibration exercises is being carried out periodically for 
the Cartosat-1 and Cartosat-2 missions to ensure the 
consistency of the data product’s accuracy. 
2.1 Approach for in-flight calibration 
The imaging geometry for both Cartosat-1 and Cartosat-2 are 
derived and characterised from measurements carried out 
during spacecraft integration, pre-launch qualification stage 
and on ground payload calibration. Changes happen due to 
environment, injection impact, temperature etc and re 
establishing the imaging geometry from image data itself is 
resorted to using in-flight geometric calibration exercises. The 
approach involves development of image-to-ground and 
ground-to- image transformations for the sensor under 
consideration in the presence of known system parameters 
(ancillary data, alignment angles, focal length etc.) and re- 
estimating some or all of these parameters with the actual 
image data and some control points. Usually, the adjustment is 
carried out using photogrammetric collinearity model for 
image-ground or ground to image transformations for deriving 
a set of platform biases or more rigorously by using resection 
approach or bundle adjustment for estimating interior(camera) 
parameters and exterior(platform) parameters. 
The in-flight geometrical calibration is based on measurements 
(observed image positions) on images from different 
cameras/strips and a few ground control points(GCPs) or 
triangulated control points(TCPs) whose ground positions are 
precisely known. In general, a comparison is made between 
observed scan, pixel positions against estimated positions for 
all GCPs or TCPs. The differences observed in image positions 
are used to statistically derive various biases and alignment 
parameters. A major problem is the unambiguous resolution of 
disparity between predicted and observed image positions. 
Also, the set of parameters (like alignment angles, Attitude 
biases, focal length etc.) which are kept floating for adjustment 
are highly correlated calling for judicious discretion for 
removing inconsistencies. The success of the adjustment 
method is decided by the location accuracy, the scale variation 
and various camera/inter camera-mounting angles and further 
confirmed with the help of post-adjustment techniques through 
multiple observations. 
Here, scan against scan differences and scan against pixel 
differences are used for deriving pitch & roll biases while pixel 
against pixel differences and pixel against scan differences 
provide estimation of focal length and yaw component 
respectively. 
2.2 Photogrammetric model 
Cartosat-1 and Cartosat-2 imaging geometry or sensor 
orientation is well represented by the conventional 
photogrammetric model. Data products s/w uses the principle 
of photogrammetric collinearity condition in image to ground 
model and in ground to image mapping through a series of 
coordinate transformations. 
r •> 
r 
~\ 
X 
X A -X S 
y 
= s M 
Y A - y s 
z 
K. 
Za - Z s y 
where (x,y,z) are image coordinates of a image point in the 
focal plane, s is scale factor, M is the transformation matrix 
between object and image space, (X A ,Y A ,Z A ) are geocentric 
coordinates of a ground point and (X S ,Y S ,Z S ) are geocentric 
coordinates of the perspective center.