Nonlinear Systematic Distortions Compensation in Satellite Images Based on an Equivalent Geometric Sensor Model Recovered From RPCs

Abstract

The rational polynomial function is widely accepted as the preferred sensor model for high-resolution satellite imagery (HRSI). However, satellite images and their associated rational polynomial coefficients (RPCs) often suffer from nonlinear systematic errors which were caused by attitude oscillation, sensor deformation, and many other imperfect calibration errors, thus affecting the geo-referencing accuracy. Instead of modeling the biases by polynomials in the image space or refining RPCs directly, this study proposes an approach of going back to the physical model and correcting the local distortions in a self-calibration block adjustment. The algorithm of an equivalent geometric sensor model (EGSM) recovery from RPCs is described in detail. As an equivalent form of the physical sensor models, EGSM reflects the complete viewing geometry of push-broom HRSI. The interior and exterior orientation parameters of EGSM can be stably recovered from RPCs without using any metadata. An approach of RPCs refinement by self-calibration block adjustment based on EGSM is introduced. This approach can effectively compensate for the nonlinear systematic errors caused by platforms and sensors similar to the approach of a rigorous sensor model. The performance of EGSM-based block adjustment is compared with the RFM-based bias compensation method. Experiments using ZY-3 images show the EGSM-based approach can effectively eliminate the nonlinear distortions in satellite images caused by sensor deformation and attitude vibrations. Furthermore, experiments using images from various satellites show that the original RFM can be well fitted with the EGSM and the residuals are smaller than 0.1 pixels for all test images.