The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
Non Local Means (Buades et al. 2005) are not able to yield 
advantage over a simple Gaussian. Only with stronger filtering 
the edge preserving nature of these algorithms becomes 
effective. However, for tie point extraction minimal filtering 
with a 3x3 pixel Gaussian window already generates on 
average about 50% more points compared to unfiltered images. 
MOLA DTM 
Exterior orientation 
> 
HRSC 
level-2 images 
▼ 
opt. low pass filtering 
▼ 
Filtered 
level-2 images 
3L_ 
Prerectification 
v 
Rectified 
level-3 images 
m * 
Image matching Level-2 coordinates 
▼ 
Image coordinates 
level-3 
▼ 
Calculation of level-2 coordinates-^ 
i 
Image coordinates 
level-2 
Figure 1. Processing chain of tie point extraction 
In the next step the images are prerectified (Norvelle 1992, 
Scholten et al. 2005) using the exterior orientation provided by 
ESOC and the MOLA DTM. The resulting ortho photos (called 
“level-3 images”) serve as input for the matching. The 
prerectification is necessary to compensate for scale differences 
caused by the elliptical orbit and for non-quadratic pixels 
caused by not perfectly adjusted integration times of the line 
scanner. As an example of the geometric differences which can 
occur a section from orbit 30 is shown in Figure 2: 
Stereo 1 channel Nadir channel Stereo 2 channel 
Level-3 image of the nadir channel 
Figure 2. Geometric differences in images of the same strip 
In the first row the crater is shown in the original level-2 images 
of the forward, nadir and backward looking channel. 
Underneath, the same area is shown in the rectified version of 
the nadir channel. During rectification information about the 
origin of the level-3 pixels with respect to the level-2 images is 
stored in separate files (labelled “Level-2 coordinates” in Figure 
1). After the matching this information is used to convert the 
level-3 image coordinates of the tie points back into the level-2 
coordinate system of the original images where the collinearity 
equations used by the bundle adjustment are defined. 
The matching employs a pyramidal approach to account for big 
parallaxes and imprecise approximate values of the exterior 
orientation. Generally, the nadir channel is matched pairwise 
with the remaining four panchromatic channels called stereo 1, 
stereo 2, photometry 1 and photometry 2. Additionally, it is 
possible to use the four colour channels blue, green, red and 
infrared if they are available at a decent resolution. Due to a 
low bandwidth between Earth and Mars for data transmission 
their resolution is often reduced via pixel binning (macro pixel). 
For example, a macro pixel format of 8 x 8 pixels decreases the 
size to l/64th compared to the original amount of data which is 
inadequate for precise point determination. 
The candidate points are distributed in a grid structure over the 
master image (nadir channel). Practical experience has shown 
that this approach is advantageous over a feature based operator 
with respect to HRSC data. This allows a more complete 
coverage with tie points and a more accurate adjustment of the 
point cloud to the MOLA DTM. An interest operator 
preferably generates points at edges which often coincide with 
breaklines in HRSC images. Because of the lower resolution of 
the MOLA dataset the differences to the HRSC points at these 
locations are higher than in flat terrain. Point transfer from the 
master image to the candidate image is carried out using the 
cartographic coordinates of the level-3 images. 
The Normalised Cross-correlation Coefficient (NCC) is used as 
similarity measure which can compensate for radiometric 
differences with respect to gain and contrast. The NCC 
assumes parallel image and object planes without elevation 
differences in the correlation window. To a large extent this 
assumption is fulfilled in the prerectified images. Therefore, it 
is possible to use large correlation windows of 35 x 35 pixels or 
bigger which are needed to capture a sufficient number of grey 
value variations in low texture imagery. 
To increase the accuracy of the points a multi-image least 
squares matching (MI-LSM) is carried out in which all points of 
a tuple are matched simultaneously. Following the approach of 
Krupnik & Schenk (1997) it is not necessary to estimate six 
affine transformation parameters per patch. As a result of the 
prerectification it is sufficient to estimate two horizontal 
translations only. The MI-LSM minimises the grey value 
differences between a particular image patch g', /=0,...,n 
(n+1 = number of image patches) and the theoretical grey 
values of the surface patch (reference patch). Ideally the 
differences are zero but in reality errors occur because of 
radiometric and geometric inaccuracies: 
G(x,y)- g' (x + dd',y + db‘)= v(x,j>) (1) 
where G(x,y) = theoretical grey values of reference patch 
g'(x, y) = image patch grey values 
da' ,db‘ = translations of patch i 
v = residuals 
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