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With the advent of NASA's lunar reconnaissance orbiter (LRO), a large amountof high-resolution digital elevation maps (DEMs) have been constructed by usingnarrow-angle cameras (NACs) to characterize the Moon's surface. However, NACDEMs commonly contain no-data gaps (voids), which makes the map less reliable.To resolve the issue, this paper provides a deep-learning-based framework forthe probabilistic reconstruction of no-data gaps in NAC DEMs. The framework isbuilt upon a state of the art stochastic process model, attentive neuralprocesses (ANP), and predicts the conditional distribution of elevation on thetarget coordinates (latitude and longitude) conditioned on the observedelevation data in nearby regions. Furthermore, this paper proposes sparseattentive neural processes (SANPs) that not only reduces the linearcomputational complexity of the ANP O(N) to the constant complexity O(K) butenhance the reconstruction performance by preventing overfitting andover-smoothing problems. The proposed method is evaluated on the Apollo 17landing site (20.0{\deg}N and 30.4{\deg}E), demonstrating that the suggestedapproach successfully reconstructs no-data gaps with uncertainty analysis whilepreserving the high resolution of original NAC DEMs.

A Neural Process Approach for Probabilistic Reconstruction of No-Data Gaps in Lunar Digital Elevation Maps