在遥感影像配准过程中,通常假设控制点是“完美的”。然而,在实际情况中,由于控制点本身不可避免的带有一定的误差导致这种假设在一定情况下并不成立,并且将会影响遥感影像几何校正的精度。普通最小二乘方法OLS(O rd inary Least Square)是遥感影像配准常用的校正估计模型,令人遗憾的是,在控制点存在误差的情况下,它的估计是有偏的,并且不能够正确传递和估计校正影像的误差大小。引入一致校正最小二乘方法CALS(ConsistentAd justed Least Squares),在此基础上提出的一个改进的方法,称之为松弛一致校正最小二乘方法RCALS(Relaxed ConsistentAd justed Least Squares)。这类回归模型具有改正控制点(解释变量)中的误差和跟踪回归模型中的误差传递的能力。为了验证CALS和RCALS模型的有效性,本文利用模拟影像进行分析。这里着重分析OLS,CALS和RCALS模型在几何校正过程中的比较。结果表明,RCALS和CALS的结果优于OLS估计结果。

Reference control points(RCPs) used to establish the regression model in registration are commonly assumed "perfect".However,this assumption is often violated in practice due to the reason that RCPs actually always containing errors.Moreover,the errors in RCPs are one of main sources lowering the accuracy of image registration of uncorrected image.In this case Ordinary least squares(OLS) estimator,widely used in the image registration of remotely sensed data,is biased and does not have the ability to handle explanatory variables with error and to propagate appropriately errors from RCPs to the corrected image.In this paper,we introduce the consistent adjusted least squares(CALS) estimator and propose a relaxed consistent adjusted least squares(RCALS) method,which can be applied to more general relationship,for registration.These estimators have good capability in correcting errors contained in the RCPs,and to propagate correctly errors of the RCPs to the corrected image with and without prior information.The objective of the CALS and our proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors.For validating CALS and RCALS estimators,we employ the CALS and RCALS estimators using real-life remotely sensed data.It has been argued and demonstrated that CALS and RCALS estimators give superior overall performances in estimating the regression coefficients and variance of measurement error.