The geographic location features of optical remote sensing images are complex and diverse, and the spatial information of multi-scale features is rich, which makes it difficult to fully extract the image features in the registration process and the registration accuracy is low. To address the above problems, an registration model that fuses contextual features and densenet is proposed, which deepens the attention to location information by embedding it in attention and integrates several different kernels of depth-separable convolution to integrate several different sensory fields to aggregate the rich multi-scale feature semantic information. Firstly, the fused densenet is used to extract the feature information from the image, then the bidirectional matching relationship is obtained using bidirectional Pearson correlation matching, and the final parameters are synthesized by weighting the bidirectional parameters obtained through regression, and finally the image registration is completed by affine transformation. The experimental results show that the proportional index coefficients of key points correctly estimated at 0.05, 0.03 and 0.01 are as high as 83.9%, 60.3% and 15.3% in the Aerial-image dataset, respectively, which effectively improves the optical remote sensing image registration accuracy.