ELunarDTMNet: Efficient Reconstruction of High-Resolution Lunar DTM From Single-View Orbiter Images

Abstract

High-resolution digital terrain models (DTMs) are critical for supporting planetary exploration missions and advancing scientific research. Recently, deep learning (DL) techniques have been applied to reconstruct high-resolution DTMs from single-view orbiter optical images, particularly for the Moon. However, DL-based methods face challenges in retrieving high-quality multiscale topographic features, especially in regions with irregular terrains or significant relief. Additionally, their generalization capability across diverse datasets is rarely evaluated. In this article, we propose an efficient DL-based single-view method with a coarse-resolution DTM as a constraint for high-quality lunar DTM reconstruction, named ELunarDTMNet. This approach introduces a hierarchical transformer-based backbone with a residual-connected mechanism, specifically designed to capture and integrate multiscale features from single-view lunar images, thereby enhancing prediction accuracy. Meanwhile, given the diverse and complex surface relief, new elevation normalization strategies are proposed to preserve terrain feature contrast while accommodating different elevation distributions. Our method performs well on diverse lunar landscapes with various topographic features and elevation changes. It outperforms the existing DL-based methods in accuracy and detail, effectively addressing their encountered challenges. Moreover, the proposed method achieves effective resolutions similar to those of the shape-from-shading (SFS) technique for subtle-scale terrain retrieval, but with enhanced elevation accuracy, illumination robustness, and approximately $850\times $ faster processing speed. Trained with the lunar reconnaissance orbiter (LRO) narrow angle camera (NAC) images, our model shows superior performance on other high-resolution lunar orbiter images, such as Chang’E-2 imagery.