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.
Highlights
Geographical and geometrical accuracy is crucial for applications employing data from multiple sources
Geometrical accuracy of remote sensing data often is ensured by geometrical transforms based on Ground Control Points (GCPs)
The corners of buildings taken as GCPs can serve as qualitative features because these are clearly distinguishable, stable in time and available in electronic maps
Summary
Geographical and geometrical accuracy is crucial for applications employing data from multiple sources. Geometrical offsets between images can introduce errors in descriptor values and burden data analysis Such problems are solved by the orthorectification procedure based on Ground Control Points (GCPs), which are well defined and recognisable features that can be located accurately in all data sets [1]. GCP manual selection by hand is a time-consuming process requiring ongoing attention; semi-automatic procedures yield an opportunity to process a large amount of data much faster and more accurately. In this context, semi-automatic procedures are solutions for selecting potential GCPs after performing the data analysis, whereas an image analyst must correct only wrong GCPs or provide GCPs in complex areas
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