Image registration plays an important role in surface detection, aerial image classification, and satellite image fusion. However, the existing image registration frameworks pay less attention to local feature information. Additionally, such frameworks have insufficient extraction of important channel and spatial information and ambiguous matching. To address these issues, we propose an end-to-end remote sensing image registration framework based on the improved attention neighborhood consistency network. Our network improves the robustness of the rotation distortion region by employing an asymmetric convolution block in the feature extraction stage, which is augmented by two one-dimensional convolution kernels with square convolution kernels. Moreover, we use a 1  ×  1 convolution to adjust the residual network channel to retain global information, realize feature reuse, and obtain multiscale image information. Next, the attention mechanism is improved by utilizing skip connections to resolve the problems of high resolution and image heterogeneity issues in remote sensing images. This allows the framework to quickly focus on important regional features and improve the network’s ability to distinguish and localize image features. In the feature matching stage, a bidirectional cross-correlation algorithm is used to establish the correspondence between the two pictures and match the corresponding points are matched. At the same time, the multilayer perceptron (MLP) structure is introduced into the neighborhood consensus network to eliminate the noise generated by mismatching. Further, it establishes a strong matching locality, prompts information aggregation at different positions and different channels, reduces registration deviation, and enhances matching robustness. The experimental results show that, overall, the algorithm outperforms comparable algorithms, extracting more accurate feature points and efficiently reducing the mismatch rate. The algorithm put forward effectively functions in a variety of situations, exhibiting high efficiency and high accuracy.