# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 3 Experimental Results
### GCP Chip Generation  
The GCP information should comprise 3D ground coordinates along with their corresponding target image coordinates. The extraction of 3D ground coordinates was performed using the aerial orthoimage and its associated DSM. Key points, uniformly distributed across the aerial orthoimages, were extracted using the Harris corner point operator [20] at specified intervals, as illustrated in Figure 7. Image chips measuring 1027 \(\times\) 1027 pixels around these key points were extracted and stored as GCP chips along with their ground coordinates.  
Subsequently, acquiring the corresponding Kompsat-3A image coordinates became necessary. These coordinates can be obtained through an image-matching technique applied between the GCP chips and the targeted Kompsat-3A images. However, performing image matching with heterogeneous data presents a challenge. Therefore, we implemented a procedure involving two distinct steps. The first step involves the image projection of  
Figure 6: GCPs sources: (**a**) aerial orthoimage (mosaic) and (**b**) DSM.  
Figure 7: Example of key point extraction from an aerial orthoimage (red triangles: GCPs).  
a GCP chip onto the targeted Kompsat-3A image. This step aligns the two datasets by minimizing geometric disparities between them. An example of this projection is depicted in Figure 8. It is important to note that the projected images adopt the Kompsat-3A satellite image coordinate systems. Although the projected aerial images closely resemble the target image, the center coordinates (indicated as 'x') still exhibit shifts due to biases in the sensor model information.  
In Figure 9, the left-most image represents an extracted aerial chip in the ground coordinate system, while the remaining images depict projections of the target Kompsat-3A images. These projections utilize the RPCs of each target image, aligning the projected images with the target Kompsat-3A images. Consequently, each projected image differs from the others owing to the varying acquisition angles of the target Kompsat-3A images.  
The second step in acquiring Kompsat-3A image coordinates involves image matching. In this study, we employed both area-matching (Figure 10) and edge-matching techniques (Figure 11) to address the substantial spectral differences between the aerial GCP chip and the targeted Kompsat-3A image [13]. These differences arise from variations in sensors, acquisition dates, weather conditions, and acquisition angles. Furthermore, the matching results should account for outlier matching points. Therefore, we implemented an outlier removal process based on data snooping [21] to detect and eliminate outliers, ensuring reliable control point information.  
Figure 8: An example of the projected chip in Image 7 (red: projected chip coverage).  
Figure 9: Alignment of the GCP chip with the target Kompsat-3A images.  
Finally, the aforementioned procedure generates dense GCP information for each Kompsat-3A image, as illustrated in Figure 12. This dense control information covers the entire image area, with the number of GCPs per scene ranging from 115 to 181, as detailed in Table 3.  
Figure 11: Image-matching example for correct target image coordinate estimation (edge-based matching) (Image 7) (red ‘\(\times\)’ and red box indicate the center and the boundary of chip).  
Figure 10: Image-matching example for correct target image coordinate estimation (area-based matching) (red ‘\(\times\)’ and red box indicate the center and the boundary of chip).