In this paper, a new star pattern recognition approach has been developed, the basic idea of which is to extract the star
pattern information, to fit together the information as a pending data set, putting all reference star information into a
criterion data set, and then to compute the relative Hausdorff distance between two sets. The pending star is recognized
according to the minimum Hausdorff distance. The approach involves most of the information of pending star
dimensional configuration. Therefore, it is strong robust for the disturbance of noise, distortion, and few meteors. In the
case of different disturbances, for instance, random noise, imaging distortion, and few meteors etc, semi-physical
simulation experimental result indicates that the approach is of good recognition effect.
In this paper, a new star pattern recognition approach has been developed, the basic idea of which is to extract the star
pattern information, to fit together the information as a pending data set, putting all reference star information into a
criterion data set, and then to compute the relative Hausdorff distance between two sets. The pending star is recognized
according to the minimum Hausdorff distance. The approach involves most of the information of pending star
dimensional configuration. Therefore, it is strong robust for the disturbance of noise, distortion, and few meteors. In the
case of different disturbances, for instance, random noise, imaging distortion, and few meteors etc, semi-physical
simulation experimental result indicates that the approach is of good recognition effect.
This paper discusses a method of the conjunction of the neighboring orbit satellite image data. It uses the plane method and surface method to search the conjunction area, and gives a mathematical model to judge the conjunction pattern. According to the step, which can connect the neighboring orbit satellite data, it gives a whole process and three methods ofconjunction. (Left Image Method, Right Image Method, Balance Image Method)
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