Ground control point (GCP) selection is a critical step in the automated high-precision geometric correction of remote sensing imagery. While the quantity, quality, and distribution of GCPs are three factors which may affect the accuracy of geometric correction, traditional automated selection methods predominantly focus on optimizing spatial distribution, often neglecting the inherent quality heterogeneity within matched point sets. This paper proposes a Reliability-weighted Spatial Coverage Sampling (SCS+R) method, which integrates matching reliability into the spatial coverage sampling framework via an adaptive weight factor (α). Experiments using Gaofen-2 (GF-2) imagery demonstrate that with 58 GCPs selected by SCS+R, the relative geometric consistency with the reference imagery is improved to a sub-pixel level (1.55–2.23 m) for multispectral images and within two pixels (0.99–1.81 m) for panchromatic images. Compared to the standard SCS, Voronoi, and weighted Voronoi methods, SCS+R improves the average accuracy by approximately 25%, 16%, and 8%, respectively. These results verify the enhanced stability and robustness of the proposed method in complex environments. Moreover, the optimal adaptive reliability weight α consistently stabilizes in a low range of 0.1–0.3, quantitatively revealing a key principle for small-sample GCP selection: spatial uniformity provides the foundation, while point reliability is the key to achieving high precision.
Wu et al. (Fri,) studied this question.