High-precision Registration Algorithm and Parallel Design Method for High-Resolution Optical Remote Sensing Images

In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images.