Robust Rational Polynomial Camera Modelling for SAR and Pushbroom Imaging

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.

Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras

“…Note that the embedding vector t j is learned from the image index j during training. 1 We find that it is better to start using β after the second epoch, when the shadow-aware shading s is already well initialized. Otherwise the model may use β to overlook shadow areas instead of trying to explain them with s. Thus, we replace (8) with (5) in the first two epochs.…”

“…Sat-NeRF casts rays directly using the RPC camera models of a set of satellite images. The RPC model is widely used for optical satellite imagery, as it allows to describe complex acquisition systems independently of satellitespecific physical modeling [1,20]. Each RPC is defined by a projection function (to project 3D points onto image pixels) and its inverse, the localization function.…”

Improving the Pair Selection and the Model Fusion Steps of Satellite Multi-View Stereo Pipelines

“…This model gives corresponding 3D/2D coordinates between the volume of interest (AOI plus height range) and the image. The correspondences are then used to adjust an RPC camera model using the RPCFIT tool [2], which fits an RPC model to the 3D/2D correspondences through a regularized least squares minimization.…”

L1b+: A Perfect Sensor Localization Model for Simple Satellite Stereo Reconstruction From Push-Frame Image Strips

“…G2D ↔ G3D as input for (Akiki et al, 2021) to fit RPC + S 1 are registered and the RPC models adjusted, both choices will yield essentially the same 3D points for a range of altitudes centered around the surface. However, for points far from the surface we should start to observe a parallax due to the fact that the scenes are acquired from different positions along the orbit.…”

“…The previous procedure results into a set of 2D-to-3D point correspondences between S + 1 and the object space, thus the RPC fitting algorithm from (Akiki et al, 2021) can be applied to produce the final RPC + S 1 model.…”