Bias Compensation for Rational Polynomial Coefficients of High-Resolution Satellite Imagery by Local Polynomial Modeling

Abstract

Abstract:The Rational Function Model (RFM) is a widely used generic sensor model for georeferencing satellite images. Owing to inaccurate measurement of satellite orbit and attitude, the Rational Polynomial Coefficients (RPCs) provided by image vendors are commonly biased and cannot be directly used for high-precision remote-sensing applications. In this paper, we propose a new method for the bias compensation of RPCs using local polynomial models (including the local affine model and the local quadratic model), which provides the ability to correct non-rigid RPC deformations. Performance of the proposed approach was evaluated using a stereo triplet of ZY-3 satellite images and compared with conventional global-polynomial-based models (including the global affine model and the global quadratic model). The experimental results show that, when the same polynomial form was used, the correction residuals of the local model could be notably smaller than those of the global model, which indicates that the new method has great ability to remove complex errors existed in vendor-provided RPCs. In the experiments of this study, the accuracy of the local affine model was nearly 15% better than that of the global affine model. Performance of the local quadratic model was not as good as the local affine model when the number of Ground Control Points (GCPs) was less than 10, but it improved rapidly with an increase in the number of redundant observations. In the test scenario with 15 GCPs, the accuracy of the local quadratic model was about 9% and 27% better than those of the local affine model and the global quadratic model, respectively.

Very High Resolution Satellite Imagery Utilization Standard for Large Scale Topographic Mapping

“…In order to improve the geometric accuracy, it is mandatory to remove bias and systematic errors contended by the RPCs. Indeed, some approaches to compensate aforementioned bias and systematic errors do exist for example by using the local polynomial modeling (Shen et al, 2017) or DEM (Alidoost et al, 2015). On the other hand, other example of VHRS imagery widely used is WorldView imagery product which has accuracies depending on the processing scheme as published by Digital Globe, 2016.…”

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

“…In Shen et al (2017b), the bias correction in RPC model is carried out using thin plate smoothing splines. In Shen et al (2017a), authors used local polynomial augmentation for RPC refinement; however, this requires very fine distribution of the GCP otherwise this may lead to poorer accuracy. We feel that only global polynomial-based bias compensation is a practically feasible choice, especially when sensor's field of view is not very small (AWiFS type).…”

Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery

“…At present, the RPC models provided by satellite image vendors are generally constructed without considering the influence of ground terrain, and are essentially a high‐precision mathematical fitting of a strict physical model (M. Wang et al, 2017). However, the satellite position and attitude parameters needed to establish a strict imaging model inevitably have systematic errors; as there is no high‐precision GCP information involved in the fitting procedure, the RPC model parameters fitted by the strict imaging model usually contain obvious systematic errors (Fraser and Hanley, 2003; Tong et al, 2010; Tang et al, 2013; Shen et al, 2017; Yang et al, 2017). As a result, the geopositioning accuracy of the RPC models provided by image vendors is often limited.…”