Multimodal Remote Sensing Image Matching Method Based on Improved Self‐Similarity Index Map and Absolute Phase Direction

ABSTRACT In multimodal remote sensing image (MRSI) matching, nonlinear radiometric distortion (NRD), scale/geometric inconsistencies, and illumination changes often cause false or missed correspondences. We propose a method that couples an improved self‐similarity index map (SSIM) with absolute phase‐orientation features. First, a feature‐weighted aggregation jointly captures similarity and edge cues. We then fuse odd‐ and even‐symmetric Log‐Gabor filters to derive phase‐congruency–based orientation and scale cues, and combine them with Sobel gradients to form a scale‐adaptive absolute phase‐congruency orientation gradient. Finally, we construct a rank‐order self‐similarity map (SRSIM) to strengthen rotational invariance. We evaluate the method on representative MRSI datasets with translation, scale, rotation, and illumination differences, and compare against five mainstream algorithms. The results show superior robustness under radiometric distortion, contrast variation, orientation reversal, and abrupt phase‐extrema changes. Quantitatively, the average number of matched points (NCM) increases by more than 40%, the average matching success rate by 38%, and the average correct matching rate by 12.23%–31.56%, while the average root‐mean‐square error (RMSE) drops to 2.12 pixels. Overall, the approach markedly improves the accuracy and robustness of automatic multimodal remote sensing image matching.