Multi-Source Remote Sensing Image Registration Method Based on Nonlinear Scale Space and Phase Congruency

Due to differences in sensor characteristics, imaging conditions, and time among multi-source remote sensing images, nonlinear changes in image radiance intensity occur, increasing the difficulty of image registration. Existing techniques often face challenges in the robustness and accuracy of feature matching when addressing these nonlinear radiance variations. To solve this problem, this paper proposes a phase congruency remote sensing image registration method based on nonlinear scale space. This method constructs a nonlinear scale space of the image using an improved anisotropic diffusion filter, effectively suppressing noise while preserving the edges and detailed features of the image. It calculates the edge strength and corner strength of the image at different scales using a phase congruency model and extracts feature points from the edge strength map and corner strength map. Multi-scale, multi-direction descriptors are constructed using the response values generated by the Log-Gabor filter to enhance the robustness of feature extraction, thereby achieving precise registration of multi-source remote sensing images. Experimental results show that this method significantly suppresses image noise and nonlinear radiance differences, and outperforms traditional algorithms and other improved registration algorithms regarding the number of correctly matched point pairs and robustness.