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

作　　者：李政[1] 李永树[1] 楚彬[1] 唐敏[1] 

机构地区：[1]西南交通大学地球科学与环境工程学院,成都610031

出　　处：《国土资源遥感》2014年第2期69-73,共5页Remote Sensing for Land & Resources

基　　金：数字制图与国土信息应用工程国家测绘地理信息局重点实验室开放基金项目(编号:DM2013SC02)

摘　　要：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.

关 键 词：无人机影像配准 多项式回归模型 EIV模型 加权整体最小二乘( WTLS) 误差统计与分析 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]