The traditional correlation coefficient matching method can achieve good image matching results for multi-source satellite images under plain terrain conditions. However, satellite images under mountainous and other complex terrain conditions are prone to mismatches. This article proposes a global probability relaxation automatic matching algorithm that takes into account geometric constraints. Based on the correlation coefficient criterion, combined with least squares matching and coarse to fine matching strategies, the algorithm utilizes the global probability relaxation conditions considering geometric constraints to achieve high-precision matching of multi-source images. A DEM automatic extraction algorithm based on this method is designed. The results of the multi-source image matching and the automatic generation of DEM by using this method are analyzed to validate the effectiveness of the algorithm.
The traditional star sensor uses the pupil function of the lens to produce circular aperture Fraunhofer diffraction to form the diffuse spot, then enlarges the diffuse diameter by defocusing, and finally uses Gaussian fitting to determine the centroid of the star point. Compared with the traditional star sensor, this paper designs a new star location method based on rectangular hole Fraunhofer diffraction. The method first changes the optical system structure of the star sensor, so that the pupil function produces Fraunhofer diffraction image on the focal plane of the optical lens at infinite distance, and then directly determines the centroid of the star point through Gaussian fitting. Finally, the reliability of this method is verified by simulating random error. The Gaussian fitting algorithm of the new star sensor has higher accuracy, less computation burden and higher accurate reliability. Experiments show that the localization accuracy of this method can reach 0.007 pixels.
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