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26 July 2007 Positional accuracy in RPC point determination based on high-resolution imagery
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Abstract
The rational function model (RFM), also known as rational polynomial coefficients (RPCs) or rational polynomial camera (RPC) model, is a generalized sensor model. Different from rigorous sensor model, RFM does not need to obtain the interior and exterior orientation geometry and other physical properties associated with the physical sensor. RFMs were first adopted by Space Imaging company as a replacement for rigorous sensor models, and it drew much attention from the commercial satellite data vendors who rapidly followed the suit in order to protect the confidential information of the sensors. This paper focuses on the solution for rational polynomial coefficients, RFM-based stereo-model reconstitution, and positional accuracy analysis. As RPCs do not have obvious physical meanings and their solution is iterative, analytical approaches to accuracy analysis may not be feasible; computer simulation is thus adopted to quantify accuracy in RPC-determined positional data. The simulation-based strategy is efficient in mapping local features in positional errors, which contain both the systematic and random components.
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Han Meng, Yongliang Liu, Jingxiong Zhang, and Hao Gong "Positional accuracy in RPC point determination based on high-resolution imagery", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675244 (26 July 2007); https://doi.org/10.1117/12.761322
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