Systematic Error Compensation Based on a Rational Function Model for Ziyuan1-02C

A rational function model (RFM) can be used directly to convert the relationships between image coordinates and object space coordinates without using any physical imaging parameters (such as satellite position and attitude). Thus, RFMs facilitate versatility and high security during geometric proce...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2015-07, Vol.53 (7), p.3985-3995
Hauptverfasser: Jiang, Yong-hua, Zhang, Guo, Chen, Peng, Li, De-ren, Tang, Xin-ming, Huang, Wen-chao
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Sprache:eng
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Zusammenfassung:A rational function model (RFM) can be used directly to convert the relationships between image coordinates and object space coordinates without using any physical imaging parameters (such as satellite position and attitude). Thus, RFMs facilitate versatility and high security during geometric processing of optical satellite imagery. Increasingly, RFMs are offered to users as the basic geolocation model for further geometric processing by imagery vendors. However, imagery vendors might perform inadequate in-orbit geometric calibrations, or the calibrated geometric parameters might not be updated in a timely manner. Thus, the RFMs may suffer from high distortion due mainly to interior errors (such as lens distortion). Using the radiometric correction products of Ziyuan1-02C panchromatic and multispectral sensor as examples, the present study addresses the compensation of systematic errors in RFMs. An undistorted RFM can be generated after calibrating the interior error compensation model once, before high-accuracy registration between the panchromatic imagery and multispectral imagery can be achieved using the undistorted RFM. Experimental evaluations based on the positioning accuracy using a few ground control points (GCPs) with an undistorted RFM matched the accuracy of the GCPs. In addition, our approach greatly improves the accuracy of registration (which surpasses 0.7 panchromatic pixels) between panchromatic and multispectral imagery.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2015.2388700