A Second-Order Spatial Guided Sampling Method for Outlier Removal in UAV Remote Sensing Image Registration

Image registration is fundamental to UAV remote sensing data processing, but repetitive textures, viewpoint and scale variations, terrain relief, and local deformation can introduce numerous outliers into tentative correspondences. The existing random-sampling and spatial-consistency methods commonly rely on pointwise confidence or isolated pairwise relations and therefore cannot reliably determine whether a locally compatible relation receives stable group-level structural support. To address this limitation, we propose SPGSC++, a second-order spatial guided sampling method for outlier removal. SPGSC++ first uses PCA to reduce the dimensionality of tentative correspondences, thereby reducing the computational burden of subsequent spatial relation construction. It then derives second-order structural quality from the common-neighbor support of directly compatible edges and uses this quality for initial data subset selection and residual-informed data subset refinement to generate and optimize a homography. Comparative experiments show that, on the five datasets CIAP, SUIRD, UAV, ZHANGZHOU, and PAN, the proposed method achieves mean Precision, Recall, F-Score, Runtime, and mean reprojection error values of 0.9627, 0.9797, 0.9702, 0.3724 s, and 1.6454, respectively. Relative to the original SPGSC, SPGSC++ increases the mean F-Score by 0.0391 and reduces Runtime and mean reprojection error by 32.39% and 58.12%, respectively. Overall, the innovation of SPGSC++ lies in using second-order common-neighbor structural quality to guide initial subset selection and residual-informed refinement for robust estimation in UAV remote sensing image registration.