LunarDepthNet: Generation of Digital Elevation Models using Deep Learning and Monocular Satellite Images

TL;DR

LunarDepthNet is a deep learning model that generates lunar surface elevation maps from monocular satellite images, aiding lunar exploration without stereo-image data.

Contribution

The paper introduces LunarDepthNet, a novel deep learning approach using UNet with EfficientNet encoder to estimate lunar DEMs from single images.

Findings

Achieved a stable loss convergence of 12% during validation.

Attained a mean nRMSE of 0.437 and MAE of 4.5 meters on test data.

Demonstrated reliable elevation map generation from monocular lunar images.

Abstract

Recent times have seen an increase in demand of high quality Digital Elevation Models (DEMs) for the lunar surface, because they are highly important for studying the moon and planning future missions. However, there is an evident lack of detailed elevation data on the Moon. To overcome this limitation, this study proposes a novel deep learning method that estimates and generates a surface elevation map directly from monocular images of the surface. The dataset used comprises of the Chandrayaan-2 Terrain Mapping Camera (TMC) images with their corresponding Digital Terrain Models (DTMs). The study proposes LunarDepthNet, which comprises of a UNet architecture to generate DEMS. It incorporates an EfficientNet encoder and custom layers to correctly learn how the light shadows on the surface relate to the actual elevation values. A combined loss function was also utilized to keep the terrain details accurate and smooth. During validation, the model showed a stable loss convergence of 12%. It achieved a mean nRMSE of 0.437 and an MAE of 4.5m in the testing stage. These results prove the model can generate dependable elevation maps from single orbital images, which are quite useful in regions of the moon where stereo-images are not available.