Aiming at the challenges of remote sensing image registration such as feature extraction difficulties caused by complex environment and registration accuracy limitations caused by multi-scale geometric deformation, this paper proposes a remote sensing image registration model that integrates dual-domain features and cross-dimensional gated attention. Firstly, the multi-scale Fourier module is designed in the feature extraction stage to improve the StarNet network structure to enhance the feature extraction capability of the model by fusing the multi-scale spatial features with the frequency domain features; then, the cross-dimensional gated attention is designed so that the model can efficiently capture the contextual information in the image without sacrificing the global sensing field; secondly, the feature matching stage bidirectional parameters are obtained by applying bidirectional matching based on partial assignment matrix, and finally, the registration is completed by affine transformation. In the experiments using the aerial image dataset, the results show that when the correctly estimated keypoint scale factor is set to 0.01, 0.03 and 0.05, the registration accuracy reaches 42.8%, 85.7% and 96.9%, respectively, and the average registration time is 0.87 s, which significantly improves the accuracy and speed of remote sensing image registration.