A Matching Method for Large-Scale Heterogeneous Remote Sensing Images with Rotation and Scaling Transformation

The automatic registration of multi-modal remote sensing data (such as optical and SAR) is a challenging task because of the significant non-linear radiation difference between these data, as well as the transformation of rotation and scaling. In order to solve the above problems, this paper proposes a two-step strategy. Firstly, the improved HOPC algorithm is introduced to obtain matching point pairs, and then a weighted strategy is used to calculate the global affine transformation matrix. In the first step, an improved Harris operator is used to extract the points of interest, and the matching accuracy is maintained while the amount of calculation is reduced. Then the matching loss calculated by the HOPC algorithm is used to calculate the contribution weight of the image block, which is utilized to calculate the global transformation matrix. The results show that the method in this paper has strong robustness to complex nonlinear radiation differences, and the two-step strategy greatly improves the accuracy of matching, which is better than similar matching algorithms in performance.