# Robust Rational Polynomial Camera Modelling for SAR and Pushbroom Imaging

arXiv: https://arxiv.org/abs/2102.13423 · HTML: https://mlsanity.com/paper/2102.13423/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 3 of 3)

- [Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras](https://mlsanity.com/paper/2203.08896/) (2022)
- [Tile and Slide : A New Framework for Scaling NeRF from Local to Global 3D Earth Observation](https://mlsanity.com/paper/2507.01631/) (2025)
- [Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences](https://mlsanity.com/paper/2607.26973/) (2026)

## Abstract (arXiv)

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.