一种鲁棒的RFM模型定位精度优化方法

A Robust RFM Model Positioning Accuracy Optimization Method

针对传统有理函数模型(RFM)系统误差补偿方法局限于姿态角误差小、相机视场角小的情况,提出了一种鲁棒性更好的卫星影像定位精度优化方法。该方法首先通过影像附带的有理多项式系数(RPC)计算出像元视线向量,再根据视线向量恢复出成像时刻虚拟外方位元素;然后对恢复的虚拟外方位元素构建误差补偿模型;最后根据控制点信息求解出模型参数后更新RPC文件。该方法从系统误差产生的原因构建补偿模型,可以规避传统方法的近似假设和条件限制。采用仿真数据和真实数据进行试验的结果表明:当姿态角误差较小时,该方法可达到与传统方法相当的精度;当姿态角误差较大时,可达到比传统方法更高的精度。

Considering the Rational Function Model(RFM) system errors compensation method is highly constrained by small error of attitude and orbit, narrow field camera, a more robust accuracy optimization method of satellite imagery is presented. Firstly, the light ray of an image point is calculated by using the Rational Polynomial Coefficients(RPC) attached to the image;secondly the pseudo exterior elements of sensor are restored when the point was acquired, then a bias compensation model is constructed for the virtual exterior elements;and finally the RPC file can be regenerated after the model parameters are solved. As the compensation model is modelled from the reason of system errors, it can avoid constraints of traditional strategy. The experimental results of simulation data and real data show that this algorithm can achieve the same level accuracy as the traditional method when the error of attitude angle is small and a higher accuracy than the traditional method can be achieved when the attitude error is large.