Research on the Geometric Precision Correction Method for Three-Axis-Stabilized Geostationary Meteorological Satellite Images Based on Landmarks

To improve the positioning accuracy of the three-axis-stabilized geostationary meteorological satellite (GSMS) images, we developed a geometric precision correction process based on landmarks. For the problem that GSMS images and binary landmark images are difficult to match accurately, we made full use of the local and global spatial information of the image pairs and proposed a dual-threshold matching algorithm based on the misalignment matrix to screen the initial matching results generated by the maximum normalized cross correlation. Under the premise of ensuring matching precision, the number of correctly matched point pairs is effectively increased to provide more sufficient samples for subsequent processing. Finally, we used a shallow neural network to establish the transformation relationship between the GSMS image and the reference image and resampled the GSMS image. We selected observations from five different periods of FY-4A/Advanced Geosynchronous Radiation Imager (AGRI) on-orbit and conducted two verification experiments: 1) in the image matching, compared with several commonly used algorithms, we found that the proposed matching algorithm has obvious advantages in matching precision and relative recall; and 2) in the image correction, the positioning error corrected by our methods is about 1.26 pixels \left ({3\sigma }\right) in the central area and about 1.30 pixels \left ({3\sigma }\right) in the edge area, which is about 14.9% and 9.1% better than the weighted iterative cubic polynomial fitting algorithm, respectively, with better robustness. In conclusion, the landmark-based geometric precision correction process proposed effectively improves the geometric accuracy of GSMS images, which is an important foundation for subsequent quantitative applications.