Rational Polynomial Coefficients Modeling and Bias Correction by Using Iterative Polynomial Augmentation

Abstract

In this article, we establish an update procedure for rapid positioning coefficients or rational polynomial coefficients (RPCs) via iterative refinements using polynomial augmentation and reference images. RPCs are widely popular in establishing a ground-to-image relationship without using physical sensor model. However, the accuracies of RPCs are degraded due to unavoidable errors in physical sensor model based on colinearity conditions. These inaccuracies essentially arise due to undulating terrain, residual errors in attitude parameters, viz. roll, pitch and yaw, inexact modeling of drift and micro-vibration, orbit error, etc. In the paper, first an initial estimate of RPCs is obtained by using \(L^2\)-regularized least square estimation. Subsequently, the RPCs are refined by using iterative affine augmentation. The RPC accuracy is further improved by a second-order polynomial augmentation. The results show that with the improved RPCs the average scan and pixel errors are within 0.5 pixel. The results of the paper are employed and validated on Resourcesat-2 imagery.