Recently, the number of high-resolution Earth-observing satellite sensors has been increasing owing to the growing needs of intelligence, mapping, and environmental monitoring. An acquired satellite image should be processed for analysis-ready data (ARD) that can be used for many applications. An important step among the processing is georeferencing that assigns geographic coordinates to each image pixel. These days, georeferencing is directly carried out using onboard sensors to produce sensor model information such as rational polynomial coefficients (RPCs). However, postprocessing is required to increase the positional accuracy of RPCs through bias compensation. Recently, bias compensation has been carried out on the basis of an automated process using ground control point (GCP) image chips. Image matching is carried out between the chips and the target satellite image to model the bias over the entire image. However, if the dissimilarity between the chip and the target satellite image increases owing to large differences in acquisition time and seasonal differences, the image matching often fails. Therefore, in this study, we utilized both intensity-based matching and edge-based matching to overcome these issues. We selected normalized cross-correlation (NCC) for intensity-based matching and relative edge cross-correlation (RECC) for edge-based matching. First, GCP chips were projected onto the target satellite images to align the two datasets. Then, both image matching methods were carried out in a pyramid image matching scheme, and their results were merged before RPC bias compensation with outlier removal. The experiments were carried out for two Kompsat-3A strips consisting of 9 and 7 scenes. NCC and RECC showed different matching results per scene, but RECC tended to show better results. NCC + RECC could derive most matching points, but the accuracy was between NCC and RECC. However, NCC + RECC shows potential to suppress a matching outlier. By applying automated bias compensation, 1.1–1.2 pixels of accuracy in root mean square error (RMSE) could be obtained.