Abstract
Abstract. On the October 24th 2007, China launched its first Lunar Probe Satellite "Chang'E I". After the 494 days travelling, the probe vehicle landed accurately at its predetermined landing site on the moon at 52.36 degrees east longitude and 1.5 degrees south latitude. 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. However, due to the dramatic change of the radiation information of the CE-1imagery, the traditional methods that are based on the gray and line characters show the limitation achieving a satisfied result. Moreover, the registration processing between lunar images that cover the whole moon has proved to be very time-consuming. To resolve the above-mentioned registration difficulties, a parallel and adaptive uniform-distributed registration method for CE-1 lunar remote sensed imagery is proposed in this paper. Based on 6 pairs of randomly selected images, both the standard SIFT algorithm and the parallel and adaptive uniform-distributed registration method were executed, the versatility and effectiveness were assessed. The experimental results indicate that: by applying the parallel and adaptive uniform-distributed registration method, the efficiency of CE-1 lunar remote sensed imagery registration were increased dramatically. Therefore, the proposed method in the paper could acquire uniform-distributed registration results more effectively, the registration difficulties including difficult to obtain results, time-consuming, non-uniform distribution could be successfully solved.
Highlights
On the October 24th 2007, China launched its first lunar Probe Satellite "Chang'E I"
For the selected feature points, the Taylor expansion is conducted first to calculate the accurate location of the local extrema and the edge-corresponding points are excluded though the Hessian matrix procedure
Since the goal of the proposed method in this research is to acquire satisfied number of the matching points rather than the quantity, and in the most circumstances the scale invariant feature transform (SIFT) descriptor generating operation is only conducted on the Gaussian octaves and DoGs at local scale, the consumed time for the image registration is significantly reduced
Summary
On the October 24th 2007, China launched its first lunar Probe Satellite "Chang'E I". 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), 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 conclusion part summarizes the work of the paper and gives a simple view of problems that need further study in this field
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