Investigation of Optimal Ground Control Point Distribution for Geometric Correction of VHR Remote Sensing Imagery

Remote sensing enables the measurement, extraction and presentation of useful information at various spatial and temporal scales. It is used by decision-makers to create sustainable projects. However, the high geometric accuracy of satellite images is vital for the accurate planning of sustainable projects and for accurately extracting information from remote sensing data. The geometric correction process for obtaining orthoimages requires a digital elevation model (DEM), ground control points (GCP) common in the object and image space, and a model that represents the transformation between the object space and the image space. Therefore, the accuracy of an orthoimage depends on the distribution of the ground control points, the model used, and the precision of the digital elevation model. In this study, the effect of the number and distribution of ground control points on the accuracy of the polynomial transformation model, rational function model and thin plate spline methods used in obtaining the orthoimage was investigated. The performance of the methods was evaluated by using a very high-resolution Pleiades-1B satellite image. The digital elevation model (DEM) was obtained by the photogrammetric method using aerial photographs. Experimental results demonstrate that the appropriate GCP distribution significantly improved the geometric correction accuracy of the orthoimages.