LAUSR

The production of high-resolution digital terrain models (DTMs) based on images is often hampered by the lack of appropriate stereo observations. Here, we propose a deep learning-based reconstruction of pixel-resolution DTMs from Lunar Reconnaissance Orbiter (LRO) single-view narrow angle camera (NAC) images, constrained by Selenological and Engineering Explorer and LRO LOLA Elevation Models (SLDEM). The procedure is carried out for a set of adjacent images, and the mosaicking of a contiguous large-area DTM is demonstrated. The approach is applied to the CE-3 and CE-4 landing sites, involving six multiple coverage and eight adjacent NAC L/R image pairs, respectively. For the DTM reconstruction, we use an improved convolutional neural network architecture with a weighted sum loss function involving three loss terms. We demonstrate that our method is robust and can deal with images acquired under varying illumination conditions. The DTM mosaic (1.5 m pixel size) covering the CE-4 landing area (72.8 × 30.3 km) is without apparent seams between the individual image boundaries and consistent with the SLDEM (60 m pixel size) in terms of overall elevation, trend, and scale, but is showing considerably more morphologic detail.