A Comprehensive Evaluation of Deep Learning-Based Image Super-Resolution for GCP Chip Matching Using High-Resolution Satellite Imagery

High-resolution (HR) Ground Control Point (GCP) chips are essential for accurate satellite image registration, yet their generation commonly relies on aerial imagery, which is often limited by data availability and update frequency. To improve the usability of satellite-derived GCP chips as an alternative, this study systematically investigates the influence of deep learning-based image super-resolution (SR) on GCP chip template matching under varying input spatial resolution conditions. GCP chips extracted from KOMPSAT-3 imagery were synthetically degraded using ×4 and ×2 downsampling, while original-resolution chips (×1) were also included for comparison. The degraded chips were enhanced using SR models and matched to KOMPSAT-3A imagery using both conventional and deep learning-based template matching methods. The experimental results show that SR is more effective for input GCP chips with limited spatial detail, with the largest improvements obtained under the ×4 downsampling condition (~2.8 m GSD). The contribution of SR is further influenced by geometric resampling during template matching and by the adopted matching method. These findings identify the conditions under which SR provides meaningful improvements and offer practical guidance for enhancing satellite-derived GCP chips, extending their usability for satellite image registration when aerial imagery is unavailable or requires frequent updates.