3. SHACKLETON RIM DTM 
This section presents the process of generating a high resolution 
DTM for the candidate landing site Shackleton Rim 1 (SR1) by 
means of photogrammetric stereo processing of line scanner 
imagery of LROC NAC. The process is divided into the three 
steps image ingestion, bundle adjustment and DTM matching. 
3.1 Image Ingestion 
The first step is searching for stereo pairs overlapping the target 
region. In support of this task a GIS database of LROC NAC 
imagery was set up by using index information available from 
PDS. A spatial query for the SRI site at -89.781°, -155.848° 
yielded about 1.300 hits at the time of writing (April 2012). For 
stereo coverage the LRO spacecraft has to carry out off-nadir 
slews of about 20° which is possible up to three times per day 
(Robinson et al. 2010). The incidence and azimuth angles 
should be as similar as possible in order to support the DTM 
matching. These constraints reduce the number of stereo pairs 
to four which come into consideration for the processing of SR1 
site. Fig. 2 displays the selected stereo pairs on top of a 
colorized LOLA shaded relief dataset. 
  
Figure 2. LROC NAC stereo pairs at SR1 site 
As can be seen the images cover the SRI site on the rim of 
Shackleton crater near the south pole of the Moon. It is striking 
that large regions within the LROC NAC strips are very dark. 
As stated in the introduction the Moon's low inclination results 
in very high incidence angles close to 90? at the poles so that 
dependent on topography and azimuth of the sun large areas are 
temporarily or permanently in shadow. Thus, generating DTMs 
at the poles of the Moon is very cumbersome. Small areas of 
LROC NAC strips have to be processed separately and 
mosaicked in order to yield a continuous DTM. After the 
download of the images from PDS ISIS routines are used for 
import, radiometric calibration and SPICE kernel initialization. 
3.2 Bundle Adjustment 
In order to improve camera pointing a bundle adjustment is 
carried out. Using ISIS tools tie points are automatically 
determined by image matching (cross correlation and least 
squares matching). Because of the large shadowed areas an 
equal distribution of tie points over the entire block was not 
obtained. However, this is desirable for line scanner sensors 
(Schmidt et al. 2008) in order to achieve fine scale 
improvements of the camera pointing. Unfortunately, only 337 
tie points have been determined so that we could not include 
spacecraft velocity, spacecraft acceleration, camera angular 
velocity and camera angular acceleration (i.e. offset and drift) 
as unknowns into the bundle adjustment. Spacecraft position 
was not included because no GCPs were measured. Only the 
camera pointing was improved (cf. Lee et al. 2012) by fitting a 
polynomial of degree 2 to the camera angles. 
The solution converged after seven iterations resulting in a 
standard deviation for the image coordinates of about 1.5 pixels. 
As the images have an average ground sampling distance 
(GSD) of about 1 m this value corresponds to an accuracy of 
about 1.5 m. Sub-pixel accuracy is desirable for an accurate co- 
registration of all input images but due to the unfavorable 
illumination conditions this aim was not feasible. Nevertheless, 
a significant improvement over the a priori accuracy of the 
LRO orbits (Mazarico et al. 2012a) could be achieved. 
3.3 DTM Matching 
The derivation of the high resolution DTM is carried out by 
digital image matching using the Ames Stereo Pipeline (ASP). 
As pre-processing steps the input images are low-pass filtered 
in order to reduce image noise. Using the LOLA dataset and the 
improved camera pointing from bundle adjustment a pre- 
rectification is carried out which crops the area of interest and 
generates quasi-epipolar images of the input pair. This step 
eliminates geometric differences between the two images with 
respect to scale, rotation and line exposure times. Additionally, 
this step also reduces perspective differences by taking into 
account the LOLA DTM which results in a considerable 
reduction of the search space for the correlation algorithm. A 
LOLA dataset with a resolution of 120m was used, dated 
March 2011; the available higher resolution versions were 
found to contain gross errors which result in severe artifacts in 
the rectified images. However, it is expected that soon a dataset 
with higher quality will be available (Mazarico et al. 2012b). 
In Fig. 3 the pre-rectified left input image cropped to the area of 
interest (SRI site) is displayed. As can be seen the area is only 
partially illuminated. To the right a 5 km long part of the crater 
rim and its permanently shadowed interior can be seen. The 
illuminated part has a width of about 1.5 km and to the left a 
temporarily shadowed area is depicted. Furthermore, it can be 
seen that due to the high incidence angle of 88.56? small 
topography features cast long shadows which disturb the 
correlation process. 
The DTM matching procedure is divided into four steps. At first 
integer disparity estimates are computed for each pixel using 
the normalized cross correlation as similarity measure. In order 
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