Research on multi-source remote sensing image registration technology based on Baker mapping

To address the issues of inaccurate estimation of registration parameters and high mismatch rate in feature based remote sensing image registration, a registration method based on global feature triangle similarity is proposed. This method utilizes the similarity principle of feature triangles to evaluate the global geometric similarity of matching feature points to eliminate mismatched points. In addition, due to the sensitivity of phase information in the frequency domain to spatial transformations and structural differences, as well as its robustness to lighting and noise, a phase structure consistency measurement method is proposed for developing feature point position adjustment strategies. The results indicate that the registration method proposed by the research institute achieved the lowest RMSE with a size of 1.51. In terms of IRMSE indicators, compared to the RANSAC measurement model, the PH SSIM measurement model has a mean decrease of 0.253. This indicates that the improved registration model proposed in the study has advantages in improving registration accuracy. The innovation of this study lies in constructing a matching feature point evaluation model to eliminate mismatched points, and proposing a remote sensing image registration method based on mismatch point removal and feature point position adjustment.