# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
###### Abstract  
The vast potential of high-resolution satellite images, including Kompsat-3A, has been demonstrated across diverse applications, such as mapping and disaster monitoring. However, these images can only be utilized as reliable CIS (geographic information system) data when they possess precise geographical information. To achieve this, sensor model information, represented by RPCs (rational polynomial coefficients), requires bias compensation through GCPs (ground control points). Though having a substantial number of well-distributed GCPs across satellite images is ideal, the acquisition process is often restricted due to cost and inaccessibility. The uniform distribution of GCP chips is not guaranteed, necessitating an investigation into the impact of GCP distribution on the bias compensation process, which is the focus of this study. Experiments were meticulously conducted using Kompsat-3A data using dense GCP information. The dense GCP information was automatically generated from aerial orthoimages through a three-step process. Firstly, the GCP chips were extracted from the aerial images, focusing on feature points. Secondly, these chips were projected onto the target Kompsat-3A data to align them accurately. Lastly, precise satellite image coordinates of the chips were obtained through image matching between the chips and the target Kompsat-3A image. The dense GCPs enabled detailed bias analysis that exhibited skewness in most Kompsat-3A data. This necessitates the implementation of an affine model for proper bias compensation over the entire image space. Next, the study delved into the influence of GCP distribution on RPC bias compensation. To this end, each target satellite image space was divided into nine zones, with the dense GCPs assigned accordingly. The accuracy of bias compensation was analyzed across nine experimental cases, ranging from GCPs occupying only one zone to GCPs covering all nine zones. It was observed that GCPs covering at least four or five zones should be utilized for reliable RPC bias compensation of Kompsat-3A, especially when aiming for a high level of accuracy with an RMSE of one pixel. Finally, it was concluded that GCPs covering three zones yielded satisfactory results as a minimum GCP requirement, but this was contingent upon their distribution not following a straight zone pattern. high-resolution satellite image; RPCs; GCPs; distribution; bias-compensation 2024  
focused on the generic bias compensation of RPCs using GCPs [2; 3; 4; 5; 6]. They emphasized the importance of GCPs for bias compensation. Additionally, many studies have tested bias compensation models for achievable accuracy in targeted high-resolution satellite images, such as IKONOS [5], QuickBird [7], Kompsat-2 [8], GeoEye-1 [9], WorldView-1/2 [10], TH-1 [11], and Ziyuan-3 [12]. While having a substantial number of well-distributed GCPs across satellite images is ideal, the acquisition process is often restricted due to cost and inaccessibility [12; 13]. Consequently, GCP chips have recently been generated from an existing database and employed for bias compensation [14]. Nevertheless, the uniform distribution of GCP chips is not guaranteed, necessitating an investigation into the impact of GCP distribution on the bias compensation process. Several studies have examined the distribution of GCPs for geometrically correcting geospatial images [12; 15; 16; 17]. One study compared various sensor models using different numbers of GCPs [12], suggesting that fixing four GCPs in the corners of the target image was optimal. Another investigation focused on the densification effect of GCPs in aerial image geometric processing [15], concluding that poor locations and distributions increased geometric errors, recommending three GCPs for cost-effective processing. In a separate study [16], the bias compensation of IKONOS RPCs for different GCP cases was explored, advocating that four GCPs suffice, saving time and costs.