Neural Radiance Fields for Multi-View Satellite Photogrammetry Leveraging Intrinsic Decomposition

High-precision geometric information derived from remote sensing scenes plays a critical role in digital surface modeling. However, acquiring such information from multi-view satellite imagery presents a challenging task due to the complexities of scene layout, illumination, and albedo. This paper presents a novel Neural Radiance Field (NeRF) that leverages intrinsic decomposition on multiview satellite image collections by inversing the photometric image formation model. Specifically, our model decomposes the satellite image into multiple intrinsic components which capture essential aspects of the scene, including albedo (reflectance property), normal (surface orientation), shadow (occlusion information), and lighting (illumination conditions). The final irradiance is derived by integrating the obtained intrinsic components via volume rendering, adhering to the physics-based photometric image formation model. Experiments on WorldView-3 images demonstrate that our model produces more photorealistic and physically plausible results for the task of novel view synthesis and digital surface modeling.