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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