LAUSR

High-resolution satellite images, including KOMPSAT, WorldView-3, and Pléiades, are widely used for mapping and environmental monitoring. In particular, satellite images with high spatial resolution have accurate location information and provide essential spatial information. Most high-resolution satellite image files are provided with rational polynomial coefficients (RPCs) that can be used for sensor modeling. However, the RPCs have an initial bias. Therefore, most satellite image platforms and researchers match satellite images to ground control point (GCP) information and perform RPCs bias compensation of images using the matching information. In Korea, an image-based GCP chip database built using orthographic images has been provided for the georegistration of various satellite images. In this manuscript, RPCs bias compensation of KOMPSAT-3A satellite images was performed using the GCP chips, and then, the possibility of automation of RPCs bias compensation through GCP chips was analyzed. Image matching, such as area-based and edge-based techniques, was used, and the results of RPCs bias compensation using the GCP chips were analyzed through experiments for various regions. The automated compensation in both area-based or edge-based matching performed well, achieving accuracy within 1.5 pixels for test areas with various topographic features. However, the forest area posed a challenge, requiring new GCP chips with rich feature information. In addition, edge-based matching showed potentials to overcome large seasonal differences, including snow cover.