Bias compensation in a rigorous sensor model and rational function model for high-resolution satellite images

This paper presents three bias-compensated models for the geometric correction of high-resolution satellite images. The proposed models include the bias-compensated rigorous sensor model (RSM) in the orbital space, the bias-compensated RSM in the image space, and the bias-compensated rational function model (RFM) in the image space. The RSM and RFM use the on-board data and sensor-oriented rational polynomial coefficients (RPCs) provided in imagery metadata, respectively. Test images include QuickBird, WorldView-1, and WorldView-2 Basic images. Experimental results indicate that the bias-compensated RSM using the zero order polynomials function in the orbital space provides higher accuracy. A comparison of the bias-compensated RSM and RFM in the image space shows that these models behave similarly, and the maximum difference in root-mean-square error is less than 0.1 m. These results show that all the proposed methods obtain accuracy of better than 1 pixel, except for the translation in the image space.