Bias Compensation for Rational Function Model Based on Total Least Squares

摘要

When using the rational function model for the geometric orientation and geopositioning of satellite imagery, systematic bias compensation for vendor-provided rational polynomial coefficients (RPCs) is very important. Most existing bias-compensation models express systematic biases as a function of certain deterministic parameters, and least squares adjustment is used for estimating correction parameters. In this paper, the errors-in-variables model is introduced to take random errors in both the observation vector and the design matrix into consideration, based on a weighted total least squares adjustment. Experiments performed with two datasets demonstrate that the proposed method is reliable and the geopositioning accuracy improvement is better compared with a traditional least squares adjustment.