用单张卫星图生成月球高精度地形图，解决缺乏详细高程数据的问题。
LunarDepthNet: Generation of Digital Elevation Models using Deep Learning and Monocular Satellite Images
- 基于UNet架构与EfficientNet编码器，结合自定义层学习阴影与高程关系。
- 测试中nRMSE为0.437，平均绝对误差4.5米，损失收敛稳定在12%。
- 适合无立体影像覆盖区域的月面测绘，对探月任务规划有实用价值。
近年来，月球表面高精度数字高程模型（DEMs）需求上升，因其对月球研究和未来任务规划至关重要。然而，月球仍缺乏详细的高程数据。为此，本研究提出一种新型深度学习方法，直接从单张月面图像生成表面高程图。数据集包含嫦娥二号地形相机（TMC）图像及其对应的数字地形模型（DTM）。提出LunarDepthNet，采用UNet结构，集成EfficientNet编码器和自定义层，以准确学习表面阴影与实际高程的关系。使用组合损失函数保持地形细节的精确性与平滑性。验证阶段模型损失稳定收敛至12%，测试阶段均方根归一化误差（nRMSE）为0.437，平均绝对误差（MAE）为4.5米。结果表明，该模型可从单张轨道图像生成可靠的高程图，在缺乏立体影像的区域尤为有用。
原文摘要 · Abstract (English)
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.
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