Affiliation:
1. Ventspils University College, Latvia
Abstract
Abstract
Geometrical accuracy of remote sensing data often is ensured by geometrical transforms based on Ground Control Points (GCPs). Manual selection of GCP is a time-consuming process, which requires some sort of automation. Therefore, the aim of this study is to present and evaluate methodology for easier, semi-automatic selection of ground control points for urban areas. Custom line scanning algorithm was implemented and applied to data in order to extract potential GCPs for an image analyst. The proposed method was tested for classical orthorectification and special object polygon transform. Results are convincing and show that in the test case semi-automatic methodology is able to correct locations of 70 % (thermal data) – 80 % (orthophoto images) of buildings. Geometrical transform for subimages of approximately 3 hectares with approximately 12 automatically found GCPs resulted in RSME approximately 1 meter with standard deviation of 1.2 meters.