Multimodal Image Matching Using Sharpened Side Phase Fusion Method

The current traditional image matching methods are difficult to adapt to multimodal image matching, in which the main influencing factors are the nonlinear radiation difference (NRD) and geometric deformation between different sensors. To address this challenge, this article proposes a multimodal matching method based on a local window filter that sharpens the edge fusion phase. The steps are listed below: 1) enhance the edge information of the image using an improved local window filter, construct the scale space of edge information enhancement, and then calculate the maximum and minimum moments of the phase congruency of the enhanced image, which constitutes the fusion moment of edge enhancement; 2) introduce sharpness features to enhance the edge structure and then weighted fusion with the edge enhancement fusion moment calculated in 1) to generate sharpened fusion phase features; and 3) combine the optimized log-polar coordinates for description, adopt the Euclidean distance as the similarity measure for matching, and finally use the fast sample consistency algorithm for mismatch rejection. This study uses a collection of multimodal remote sensing images showing NRD and geometric deformation, with comparative experiments conducted using LNIFT, OSS, OS-SIFT, RIFT, and CMM-Net methods. The experimental results show that the method proposed in the article outperforms other similar algorithms in terms of the number and precision of matching points. The number of correctly matched points is improved by more than two times on average, and the root mean square error (RMSE) reaches an average of 1.24 pixels, which proves the algorithm has better robustness and accuracy.