Bias-corrected rational polynomial coefficients for high accuracy geo-positioning of QuickBird stereo imagery

The rational function model (RFM) is widely used as an alternative to physical sensor models for 3D ground point determination with high-resolution satellite imagery (HRSI). However, owing to the sensor orientation bias inherent in the vendor-provided rational polynomial coefficients (RPCs), the geo-positioning accuracy obtained from these RPCs is limited. In this paper, the performances of two schemes for orientation bias correction (i.e., RPCs modification and RPCs regeneration) is presented based on one separate-orbit QuickBird stereo image pair in Shanghai, and four cases for bias correction, including shift bias correction, shift and drift bias correction, affine model bias correction and second-order polynomial bias correction, are examined. A 2-step least squares adjustment method is adopted for correction parameter estimation with a comparison with the RPC bundle adjustment method. The experiment results demonstrate that in general the accuracy of the 2-step least squares adjustment method is almost identical to that of the RPC bundle adjustment method. With the shift bias correction method and minimal 1 ground control point (GCP), the modified RPCs improve the accuracy from the original 23 m to 3 m in planimetry and 17 m to 4 m in height. With the shift and drift bias correction method, the regenerated RPCs achieve a further improved positioning accuracy of 0.6 m in planimetry and 1 m in height with minimal 2 well-distributed GCPs. The affine model bias correction yields a geo-positioning accuracy of better than 0.5 m in planimetry and 1 m in height with 3 well-positioned GCPs. Further tests with the second-order polynomial bias correction model indicate the existence of potential high-order error signals in the vendor-provided RPCs, and on condition that an adequate redundancy in GCP number is available, an accuracy of 0.4 m in planimetry and 0.8 m in height is attainable.