  Most current NeRF variants for satellites are designed for one specific scene
and fall short of generalization to new geometry. Additionally, the RGB images
require pan-sharpening as an independent preprocessing step. This paper
introduces psPRF, a Planar Neural Radiance Field designed for paired
low-resolution RGB (LR-RGB) and high-resolution panchromatic (HR-PAN) images
from satellite sensors with Rational Polynomial Cameras (RPC). To capture the
cross-modal prior from both of the LR-RGB and HR-PAN images, for the
Unet-shaped architecture, we adapt the encoder with explicit
spectral-to-spatial convolution (SSConv) to enhance the multimodal
representation ability. To support the generalization ability of psRPF across
scenes, we adopt projection loss to ensure strong geometry self-supervision.
The proposed method is evaluated with the multi-scene WorldView-3 LR-RGB and
HR-PAN pairs, and achieves state-of-the-art performance.
