基于加权整体最小二乘法的无人机影像配准

UAV image registration based on the weighted total-least-squares

在经典的遥感图像配准中,多项式回归模型一般假设参考控制点(RCPs)是没有误差的。然而,实际情况是RCPs含有误差,并且不同图像之间RCPs残差中误差也不尽相同。通常,最小二乘(LS)方法仅考虑观测向量中的误差,而整体最小二乘(TLS)方法则同时考虑观测向量和系数矩阵的误差,并假设它们具有相同的残差中误差。针对上述情况,引入更为合理的加权整体最小二乘(WTLS)方法对多项式回归系数进行估计。实验结果表明,与LS和TLS方法相比,WTLS方法能够更好地求取几何变换的多项式系数,其图像配准精度明显提高。

In optical image registration, the polynomial regression model generally supposes that the reference control points （ RCPs） used as the coefficient matrix is error -free. However, the actual RCPs often inevitably contain errors and RCPs residual errors between different images are not the same. The general least squares method （ LS） only considers the error in the observation vector whereas the total least squares method （ TLS） takes the errors of both the observation vector and the coefficient matrix into account and assumes that they have the same residual error. In view of this situation, this paper introduces a more reasonable weighted total least squares method （WTLS） for polynomial regression coefficients estimation. Experiments show that the WTLS can estimate the parameters better and significantly improve the image registration accuracy.