First Experience with Figure Condition Analysis Aided Bias Compensated Rational Function Model for Georeferencing of High Resolution Satellite Images

Georeferencing of high resolution satellite images using sensor-dependent Rational Function Model (RFM) is a common approach in the remote sensing community since the turn of the millennium. In the case of mono image evaluation, the georeferencing is performed using the ground control points (GCPs), and the image-wide georeferencing accuracy is estimated at the independent check points (ICPs). Nevertheless, such an accuracy assessment approach has some disadvantages and must be overcomed by a proper method as suggested by the figure condition analysis (FCA). Considering various bias compensation methods, the FCA is adopted to RFM and a case study is performed on three high resolution satellite images (HRSIs), IKONOS Geo, QuickBird OrthoReady Standard and OrbView-3 Basic, covering undulating and mountainous Zonguldak test site. The results demonstrate that a bias compensation is required for all images, and IKONOS has the highest accuracy both at GCPs and figure condition points (FCPs) where OrbView-3 has the lowest accuracy. The innovative characteristics of FCA and further research issues are also discussed.