Robust Rational Polynomial Camera Modelling for SAR and Pushbroom Imaging

TL;DR

This paper presents a terrain-independent algorithm for accurately deriving Rational Polynomial Camera (RPC) models from 3D-2D point correspondences, applicable to SAR and optical sensors, enhancing remote sensing image analysis.

Contribution

It introduces a regularized least squares-based algorithm to derive RPC models from various data sources, improving flexibility and accuracy in remote sensing applications.

Findings

The method performs well across different sensor types.

It effectively derives RPCs from physical and existing models.

The approach is robust to variations in point correspondences.

Abstract

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.