Registration of Remote Sensing Image with Measurement Errors and Error Propagation

Abstract

Reference control points (RCPs) used to establish the regression model in registration or geometric correction are commonly assumed “perfect”. However, this assumption is often violated in practice due to RCPs actually always containing errors. Moreover, the errors in RCPs are one of main sources lowering the accuracy of geometric correction of uncorrected image. In this case Ordinary least squares (OLS) estimator, widely used in geometric correction 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 geometric correction or 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 demon-strated that CALS and RCALS estimators give superior overall performances in estimating the regression coefficients and variance of measurement error.