Photogrammetry for Unconstrained Optical Satellite Imagery With Combined Neural Radiance Fields

We introduce a novel method tailored for unconstrained multiview optical satellite photogrammetry in time-varying illumination and reflection conditions. Our approach uses continuous radiance fields to represent surface radiance and albedo based on radiometry principles, integrating both static and transient components for satellite photogrammetry. In addition, an innovative self-supervised mechanism is introduced to optimize the learning process which leverages dark regions' accentuation, transient and static composition, and shadow regularization. Evaluations on multidate WorldView-3 images affirm that our model consistently surpasses the state-of-the-art techniques.