An SAR Image Registration Algorithm Based on Edge Intersection Extraction and Retrained HardNet

Image registration plays a pivotal role in various image processing applications, which is widely used in image fusion and change detection. However, The presence of speckle noise in SAR images causes a primary reduction in registration accuracy and existing algorithms have not achieved high-precision registration while maintaining low computational complexity. This letter proposes an image registration algorithm based on edge intersections and deep-learning descriptors. An edge-directed voting mechanism is introduced to identify corner points, and a custom SAR image dataset is constructed to retrain the HardNet descriptor network. Experimental results validate the superiority of the proposed method in terms of robustness and accuracy, achieving SAR image registration on a self-constructed dataset with an RMSE of 0.38, showcasing the utmost registration accuracy, while maintaining lower computational complexity than traditional approaches.