R₂FD₂: Fast and Robust Matching of Multimodal Remote Sensing Images via Repeatable Feature Detector and Rotation-Invariant Feature Descriptor

Identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R2FD2) that is robust to radiation and rotation differences, which consists of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the multichannel autocorrelation of the log-Gabor (MALG) is presented for feature detection, which combines the multichannel autocorrelation strategy with the log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the rotation-invariant maximum index map of the log-Gabor (RMLG), which includes the fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a rotation-invariant maximum index map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to improve the resistance of RMLG to radiation and rotation variances. Finally, we conduct experiments to validate the matching performance of our R2FD2 utilizing different types of multimodal image datasets. Experimental results show that the proposed R2FD2 outperforms five state-of-the-art feature matching methods. Moreover, our R2FD2 achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over contrastive methods.