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Bias Compensation for Rational Function Model Based on Total Least Squares
Author(s) -
Yu Anzhu,
Jiang Ting,
Guo Wenyue,
Jiang Gangwu,
Wei Xiangpo,
Zhang Yi
Publication year - 2017
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12183
Subject(s) - least squares function approximation , rational function , generalized least squares , function (biology) , polynomial and rational function modeling , compensation (psychology) , polynomial , non linear least squares , design matrix , mathematics , algorithm , explained sum of squares , computer science , statistics , linear model , estimator , psychology , mathematical analysis , evolutionary biology , psychoanalysis , biology
When using the rational function model for the geometric orientation and geopositioning of satellite imagery, systematic bias compensation for vendor‐provided rational polynomial coefficients ( RPC s) 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.