ON METHOD 
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1. INTRUDUCTION 
On the October 24" 2007, China launched its first lunar Probe 
Satellite "Chang'E 1". After the 494 days travelling, the probe 
vehicle landed at its predetermined landing site on the moon at 
52.36 degrees east longitude and 1.5 degrees south latitude 
accurately (Li et al., 2010). It sent back the first imagery of the 
lunar surface on 26 November 2007 and accomplished all the 
scheduled scientific tasks successfully. As the first lunar Probe 
Satellite, the major goal of Chang'E I mission is to obtain three- 
dimensional images of the landforms and geological structures 
of the lunar surface, so as to provide a reference for planned 
future soft landings (Zheng et al., 2007). 
Due to the dramatic change of the radiation information of the 
CE-limagery, the traditional gray and line characters based 
matching method has shown the limitation achieving a satisfied 
result. By analyzing the imaging principle and quality of the 
CE-1 satellite, the Scale Invariant Feature Transform (or SIFT) 
algorithm is chosen as the rescarch-based algorithm in this 
paper. SIFT is an algorithm mainly applied in computer vision 
to detect and describe local features in images. The SIFT 
algorithm was published by David Lowe in 1999 (David G,1999) 
and then improved in 2004 (David G, 2004). One important 
characteristic of the SIFT algorithm is that its feature descriptor 
is invariant to uniform scaling, orientation, and partially 
invariant to affine distortion and illumination changes 
(Mikolajczyk and Schmid, 2005, Sun, 2005), therefore shows 
the suitability in image matching when great gray value 
differences exist. Both the traditional gray-based and the SIFT 
image matching algorithm are conducted for the CE-1 lunar 
imagery matching in this research, the experimental results 
indicate the efficiency and applicability of SIFT for feature 
extraction and matching (Liu, 2009, Liu 2010). 
The rest of the paper in organized as follows. The imaging 
principle of the CCD stereo camera of CE-1 satellite and the 
core theory of the SIFT matching algorithm are presented first. 
To improve the efficiency of the matching operation and solve 
the uneven distributed matching points, the paralleled matching 
method is further studied in this paper and then a parallel and 
adaptive uniform-distributed registration method for Chang'e-1 
lunar remote sensed imagery is proposed. Based on the 6 pairs 
of randomly selected images, the performance of the proposed 
strategy through the comparison of its experimental results with 
the results generating from the standard SIFT algorithm is 
evaluated. Finally, the conclusion part summarizes the work of 
the paper and gives a simple view of problems that need further 
study in this field. 
2. IMAGING PRINCIPLE OF CE-1 SATELLITE AND 
SIFT ALGORITHM 
21 Imaging principle of CCD stereo camera on CE-1 
The CE-1 satellite is assembled with a three-liner-array CCD as 
the receiving device and designed with a orbit altitude of 200km. 
The three line-array CCD are used to collect the forward, nadir 
and backward looking of the imaging track separately while the 
off-nadir viewing across the track is 17° (Wang et al., 2008, 
Zhao et al., 2009, ), see Figure 1(left). The CE-1camera has the 
Pixel size of 14um and is designed with a 23.33mm’s focal 
length. In the camera imaging process, the arrays of 11, 512 and 
1013 of the CCD matrix take ground features at the same time, 
% presented in Figure I (right). This scanning and imaging 
Method determines the high overlapping characteristic of the 
Stripes among the forward, nadir and backward view, which 
could be up to 96%. 
Flying Direction — 
   
Figure 1 Imaging principal of CCD camera (left) , imaging 
principal of CCD camera (right) 
The radiation information of the CE-1 lunar imageries changes 
violently. The pictures below represent the radiation variation of 
the CE-1 images captured from different areas: (1) low radiation 
area, (2) high radiation area, and (3)(4) high radiation contrast 
in the continuous area. This phenomenon results in difficulties 
in image matching by using the traditional gray-based or the 
feature point and feature line characteristic based methods. 
    
(1) (4) 
Figure 2 Comparison of radiation information from different 
areas on CE-1 images 
2.2 SIFT algorithm 
The scale invariant feature transform (SIFT) algorithm, was 
firstly proposed by Lowe in 1999 and then further developed in 
2004 . The core ideology of SIFT algorithm is locating the local 
extrema at the scale space and further detecting the invariant 
image features. Compared with the feature-based algorithm, the 
SIFT is invariant to image translation, scaling, rotation and 
partially invariant to illumination changes and affine projection. 
SIFT image registration is conducted through the following five 
steps. 
1) Generating Gaussian and DOG images at Scale-space. 
Gaussian kernel is the only linear kernel for scale 
transformation (Koenderink, 1984, Lindeberg, 1994). To build 
the DOG pyramid the input images are up-sampled and 
convolved iteratively with a Gaussian kernel of c-1.5. After 
this step, 5 Gaussian pyramid are generated and each of them 
contains 5 layers, thus the Difference of Gaussians (DOG) 
pyramid can be computed by conducting the differential 
operation onto the two nearby images. 
2) Extrema point location and marginal point elimination. 
The local extrema point is detected by comparing with its 
neighboring 8 points in the same scale level, 9 neighboring 
points in the scale above and scale below each. In the DOG 
processing, the local maximum or minimum is considered as the 
candidate till all the 26 neighboring points are assessed. The 
SIFT algorithm works by fitting a 3D quadratic function to 
define the location and the scale of the accurate key-points 
(reaching the sub-pixel accuracy), and discarding the low- 
contrast such as the unstable edge-corresponding points. For the 
selected feature points, the Taylor expansion is conducted first 
to calculate the accurate location of the local extrema and then 
the edge-corresponding points are excluded though the Hessian 
matrix procedure. 
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