Satellite true digital orthophoto map generation without elevation data: a New NeRF-based method

Abstract

ABSTRACT Existing satellite true digital orthophoto map (TDOM) generation methods are designed for ground elevation data. Obtaining large-scale and high-precision ground 3D data is prohibitively costly, while low-precision elevation data introduces issues such as relief displacement, boundary distortion, and artefacts. Furthermore, producing TDOMs from satellite images captured under various lighting conditions can cause colour inconsistency problems. These issues impose limitations on the application and development of satellite TDOMs. In this paper, we propose a novel image-to-image approach that directly generates high-quality TDOMs from multi-view satellite images without the need for elevation data as input. Specifically, the 3D scene is efficiently represented by the volume density and colour, which are modelled utilizing a neural network. During each iteration, this 3D representation undergoes optimization by the multi-view satellite signals, employing a volume rendering formula. Finally, TDOM is produced utilizing our true ortho-volume rendering technique. Experimental results demonstrate that our TDOM achieves superior visual quality and geometry accuracy without the need for 3D elevation data.