GLIFT: A Global-to-Local Invariant Feature Transformation Method for Multimodal Remote Sensing Image Matching

The multimodal remote sensing image matching is crucial for many applications. However, nonlinear intensity distortion (NID) significantly impairs matching performance, especially when dealing with scale and rotation variations. To address this challenge, we propose a global-to-local invariant feature transformation (GLIFT) method for multimodal remote sensing image matching. The method consists of three key components: feature detection, global search, and local search. First, we introduce a fast dominant orientation assignment approach, which ensures rotational invariance while reducing computational costs. Next, we design a 3-D descriptor structure that effectively integrates both local region information and keypoint self-information, enhancing the robustness and discriminability of the descriptor. To overcome the limitations of image pyramids in handling scale variations in multimodal remote sensing images, we propose a local multiregion description strategy that adapts well to scale changes. In addition, we construct a pixel-based descriptor vector and present a local search strategy to identify optimal matching point pairs within local regions, which effectively improves the matching accuracy. Finally, we validate the matching performance of GLIFT by conducting experiments and comparing it with eight state-of-the-art multimodal matching algorithms on various datasets. Extensive results demonstrate that our method effectively addresses the challenges posed by rotation and scale variations in multimodal remote sensing images. It significantly enhances the number of correct matches (NCMs), matching accuracy, and matching precision. Our code is available at: https://github.com/wdzsc/GLIFT