Robust Rational Polynomial Camera Modelling for SAR and Pushbroom Imaging
This work addresses the need for generic and accurate camera modeling in remote sensing, but it appears incremental as it builds on existing RPC methods with a new algorithm.
The paper tackles the problem of accurately deriving Rational Polynomial Camera (RPC) models for remote sensing imaging systems like SAR and optical sensors, by proposing a terrain-independent algorithm based on regularized least squares fit, and tests it on SAR and optical data with varying point correspondences and area sizes.
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.