Bias Compensation in a Rigorous Sensor Model and Rational Function Model for High-Resolution Satellite Images

摘要

This paper presents three bias-compensated modelsfor 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.