Abstract:
Conducting single frame orthorectification on satellite images to create an ortho-image requires four basic components, namely an image, a geometric sensor model, elevation data (for example a digital elevation model (DEM)) and ground control points (GCPs). For this study, orthorectification was executed numerous times (in three stages) and each time components were altered to test the geometric accuracy of the resulting ortho-image. Most notably, the distribution and number of ground control points, the quality of the elevation source and the geometric sensor model or lack thereof were altered. Results were analysed through triangulating and comparing the geolocation accuracy of the ortho-images. The application of these different methods to perform orthorectification encompass the aim of this paper, which was to investigate and compare the positional accuracies of ortho-images under various orthorectification scenarios and provide improved geometric accuracies of VHR satellite imagery when diverse ground control and elevation data sources are available. By investigating the influence that the distribution and number of GCPs and the quality of DEMs have on the positional accuracy of an ortho-image, it became clear that a reasonable increase in the number of uniformly distributed GCPs combined with progressively accurate DEMs will ultimately improve the quality of the orthorectified product. The results also showed that when more GCPs were applied, the smaller the difference in accuracy was between the different DEMs utilised. It was interesting to note that when it is suitable to manually collect well-distributed GCPs using a GPS handheld device over the study area then a very accurate result can be expected. Nonetheless, it is also important to note that if it is not possible/practical to achieve the latter, satellite based GCP collection do provide a very good alternative. It was also determined that utilising GCPs which were extracted from vector road layers to only cover specific areas in the image scene produced less favourable results. Several contributions towards improved orthorectification procedures were made in this study. These include the development of an automatic GCP extraction script (A-GCP-ES), written in the Python scripting language with the purpose to ease the process of manually placing GCPs on an input image when repeatedly performing orthorectification.
