ISPRS Commission III, Vol.34, Part 3A ,,Photogrammetric Computer Vision, Graz, 2002 
  
the others at the same time. Those four images can be 
integrated into a composite image or viewed individually. The 
CAMIS sensor has been used in multispectral imaging and 
mapping purposes by mounting it in an airplane with GPS and 
INS systems. These auxiliary sensors provide very good 
position and attitude data for stabilizing the subsequent bundle 
block adjustment. The calibration procedure required a number 
of steps and they are summarized below. 
2.1 Site preparation 
The idea of the calibration was to layout some targets in the 
object space, locate them accurately, and acquire an image of 
those targets by the sensor. Then, we relate the coordinates of 
the targets in both systems, image and object space, in order to 
obtain the camera parameters. So, the procedure starts by 
setting up the calibration site. First, we designed the targets to 
be cross shapes so their center positions will be obtained very 
easily. Then they have been laid out in an “X” pattern that 
allows us to recover the needed geometric parameters and 
systematic errors as shown in figure 2. Those targets were 
placed on an almost flat service wall and the sensor position 
was located around 8 meters away from that wall (at that 
distance we could use the “infinity” focus position. In order to 
register the objects in the scene, an object space coordinate 
system was established at the site and two other instrument 
stations were marked to use in the measurements. 
  
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Figure 2. Targets layout 
2.2 Measurements and adjustment in object space 
Two three arc-second theodolites were mounted at the 
referenced stations and were used to measure directions to all 
relevant objects of the network: target centers, theodolite 
locations, and camera case monuments. The origin of the object 
space was chosen to be theodolite one at station one at the right 
of the sensor position. Many manual measurements were made 
of the camera physical layout, using machinist calipers. The 
lenses were also placed on an optical bench for determining the 
locations of the nodal points. A cross section of one camera is 
shown in figure 3. This was needed to locate the camera front 
nodal point with respect to the camera body, which would be 
located in the network by theodolite observations. The spacings 
between the wall targets were measured with a steel tape. 
Having all these observations, we end up with an 
overdetermined system of equations. We developed a bundle 
program to simultaneously adjust the theodolite and distance 
observations. As a result of that, we determined all of our 
targets and camera stations in the referenced coordinate 
system. The next step was capturing images by the sensor(s). 
  
  
  
Front nodal 
point | 
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| Camera | LI 
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| = 16 mml| nodal points are 
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Rearnodal their usual position 
point 
  
  
  
Figure 3. Cross section of one camera showing the 
lenses, rear and front nodal points 
2.3 Capturing images and obtaining image space 
coordinates 
Before the measurements, the sensor was mounted on a leveled 
plate fixed on a survey tripod. In this sense, the exposure 
stations were fixed and predetermined to an accuracy of a few 
millimeters. In this step we tried to simulate the real working 
conditions by setting the lenses to the “working” infinity focus 
position. Images were viewed after captured to verify 
acceptable radiometry. With the band pass filters our 
illumination setup was just sufficient to produce acceptable 
image definition for the targets. In the future, stronger light 
sources would be used to allow more flexibility. After this 
step, the laboratory work ended and the processing procedure 
started. 
2.4 Image space calculations 
Once the images were captured, we ran a cross correlation 
matching program to get rough approximation of target 
positions in the image space to within a pixel. The cross 
correlation matching function works by computing the 
similarity between two same sized widows (Mikhail, Bethel 
and McGlone, 2001; Mikhail and Ackerman, 1976). One 
window patch contains the ideal target and the other contains a 
window from the image. In general, a matching problem is a 
key algorithm for other applications and image analysis. 
Despite the fact that, the cross correlation matching results 
showed that we are only away from the exact position by a 
pixel or less, we needed more accurate and precise methods to 
guarantee the sub-pixel precision. This level of precision is 
necessary for a camera calibration problem. Least squares 
matching (LSQM) is very adequate technique for this purpose. 
LSOM utilizes the first derivative of the intensity in both x and 
y directions to obtain the best correspondence and the exact 
matching can be reached by moving one window with respect 
to the other one (Atkinson, 1996). Some obstacles, such as 
radiometric effects, were faced and solved by modifying the 
algorithm. As a result of this step, the image space coordinates 
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