Assessment of geometric correction of remote sensing satellite images using RPC versus GCP

Abstract

Geometric distortions are common problems when dealing with remote sensing satellite images. Therefore, geometric correction is a necessary process for preparing remote sensing satellite images for many applications. Physical sensor model is formed by integrating the geometry of imaging sensor and positioning sensors as GPS and star trackers with system calibration parameters. Physical sensor model is not available in common but a Rational Polynomial Coefficient (RPC) model is provided as an alternative representation of sensor model. The RPC model is used for geometric correction of satellite image to get the spatial data of image features. The accuracy of the resultant image depends on the accuracy of RPC model which is not known as common. The research objective is to assess the accuracy of geometric correction of the satellite image using RPC model versus geometric correction using Ground Control Points (GCPs), along with their effect on the final accuracy of the output spatial features. The available data is IKONOS-2 image with its RPC file and seven GCPs with high accuracy obtained from ground survey for the study area. The input image is corrected using two available data separately. First degree of polynomial is used for transformation process for the case of GCPs. Bilinear interpolation technique is used to determine the pixel value of the newly resultant corrected images for the two cases. GCPs is preferred when available because the resultant image with RPC geometric correction has 15.0 meters average linear error while 3.0 meters error in case of GCPs geometric correction.