CAJ | 학술논문

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。
재경전적요감도상배준중,다항식회귀모형일반가설삼고공제점( RCPs)시몰유오차적。연이,실제정황시RCPs함유오차,병차불동도상지간RCPs잔차중오차야불진상동。통상,최소이승( LS)방법부고필관측향량중적오차,이정체최소이승( TLS)방법칙동시고필관측향량화계수구진적오차,병가설타문구유상동적잔차중오차。침대상술정황,인입경위합리적가권정체최소이승( WTLS)방법대다항식회귀계수진행고계。실험결과표명,여LS화TLS방법상비,WTLS방법능구경호지구취궤하변환적다항식계수,기도상배준정도명현제고。
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.