LAUSR

Ground control points play an important role in improving the positioning accuracy of satellite images. At present, most control points must be obtained by manual deployment (calibration field) or feature extraction. The control points obtained by manual deployment are fixed in certain areas and have a high deployment cost. The current method for feature matching is limited by the acquisition of the reference image and matching accuracy, which results in poor flexibility and is not conducive to improvements to worldwide satellite positioning precision. To solve this problem, this study proposes a new automatic generation and application algorithm for landmark control points across the globe based on the deep learning method. Using this method, landmarks can be selected and the deep-learning-based target-detection method can be used to realize the automatic generation of control points. When satellite images with relatively low positioning precision are used, landmark control points can be accurately obtained with a precision reaching the sub-pixel level, which can provide a sufficient foundation for the geometric correction of non-mapping satellite images. In this study, a remote sensing image dataset of road intersections was also constructed, which considers road intersections as landmarks. The experiment is carried out with the road intersection dataset, and CenterNet network is trained. The experiments show that the detection precision of the network can reach 96.27%. Finally, we designed an application strategy for the landmark control points and improved the image matching method, such that the matching precision between the landmark images and images to be processed can reach the sub-pixel level and conform to the requirements of geometric correction for non-mapping satellite images.