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Remote Sens. 2017, 9(3), 200; doi:10.3390/rs9030200

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

1
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-Information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services, Shenzhen University, Nanhai Road 3688, Shenzhen 518060, China
2
College of Information Engineering, Shenzhen University, Nanhai Road 3688, Shenzhen 518060, China
3
Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Chandra Giri and Prasad S. Thenkabail
Received: 23 October 2016 / Accepted: 22 February 2017 / Published: 24 February 2017
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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. View Full-Text
Keywords: bias correction; georeferencing; local regression; photogrammetry; rational function model; satellite image bias correction; georeferencing; local regression; photogrammetry; rational function model; satellite image
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shen, X.; Li, Q.; Wu, G.; Zhu, J. Bias Compensation for Rational Polynomial Coefficients of High-Resolution Satellite Imagery by Local Polynomial Modeling. Remote Sens. 2017, 9, 200.

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