  The Rational Polynomial Camera (RPC) model can be used to describe a variety
of image acquisition systems in remote sensing, notably optical and Synthetic
Aperture Radar (SAR) sensors. RPC functions relate 3D to 2D coordinates and
vice versa, regardless of physical sensor specificities, which has made them an
essential tool to harness satellite images in a generic way. This article
describes a terrain-independent algorithm to accurately derive a RPC model from
a set of 3D-2D point correspondences based on a regularized least squares fit.
The performance of the method is assessed by varying the point correspondences
and the size of the area that they cover. We test the algorithm on SAR and
optical data, to derive RPCs from physical sensor models or from other RPC
models after composition with corrective functions.
