End-to-end Registration Algorithm for High-resolution Multi-view SAR Images

Due to the side-looking and coherent imaging mechanisms, feature differences between high-resolution Synthetic Aperture Radar (SAR) images increase when the imaging viewpoint changes considerably, making image registration highly challenging. Traditional registration techniques for high-resolution multi-view SAR images mainly face issues, such as insufficient keypoint localization accuracy and low matching precision. This work designs an end-to-end high-resolution multi-view SAR image registration network to address the above challenges. The main contributions of this study include the following: A high-resolution SAR image feature extraction method based on a local pixel offset model is proposed. This method introduces a diversity peak loss to guide response weight allocation in the keypoint extraction network and optimizes keypoint coordinates by detecting pixel offsets. A descriptor extraction method is developed based on adaptive adjustment of convolution kernel sampling positions that utilizes sparse cross-entropy loss to supervise descriptor matching in the network. Experimental results show that compared with other registration methods, the proposed algorithm achieves substantial improvements in the high-resolution adjustment of convolution kernel sampling positions, which utilize sparse cross-entropy loss to supervise descriptor matching in the network. Experimental results illustrate that compared with other registration methods, the proposed algorithm achieves remarkable improvements in high-resolution multi-view SAR image registration, with an average error reduction of over 65%, 3～5-fold increases in the number of correctly matched point pairs, and an average reduction of over 50% in runtime.