With the advent of NASA's lunar reconnaissance orbiter (LRO), a large amount of high-resolution digital elevation maps (DEMs) have been constructed by using narrow-angle cameras (NACs) to characterize the Moon's surface. However, NAC DEMs commonly contain no-data gaps (voids), which makes the map less reliable. To resolve the issue, this paper provides a deep-learning-based framework for the probabilistic reconstruction of no-data gaps in NAC DEMs. The framework is built upon a state-of-the-art stochastic process model, attentive neural processes (ANP), and predicts the conditional distribution of elevation on the target coordinates (latitude and longitude) conditioned on the observed elevation data in nearby regions. Furthermore, this paper proposes sparse attentive neural processes (SANPs) that not only reduce the linear computational complexity of the ANP $\mathcal{O}(N)$ to the constant complexity $\mathcal{O}(K)$ but enhance the reconstruction performance by preventing over-fitting and over-smoothing problems. The proposed method is evaluated on three different lunar NAC DEMs with distinct geographical features, including the anthropogenic site, graben, and craterlet, demonstrating that the suggested approach successfully reconstructs no-data gaps with an uncertainty analysis while preserving the high-resolution of original NAC DEMs.

随着NASA的月球侦察轨道器（LRO）的出现，通过使用窄角相机（NAC）来表征月球表面，已经构造了大量的高分辨率数字高程图（DEM）。但是，NAC DEM通常包含无数据间隙（空隙），这使地图的可靠性降低。为了解决这个问题，本文为NAC DEM中的无数据缺口的概率重建提供了一个基于深度学习的框架。该框架建立在最新的随机过程模型，注意力神经过程（ANP）之上，并根据附近地区观测到的海拔数据预测目标坐标（纬度和经度）上海拔的条件分布。此外，本文提出了稀疏的注意神经过程（SANP） 不仅降低了ANP的线性计算复杂度 $\mathcal{Ø}（ñ）$ 不断复杂 $\mathcal{Ø}（ķ）$但是通过防止过度拟合和过度平滑的问题来提高重建性能。在三种不同的具有不同地理特征的月球NAC DEM上评估了所提出的方法，包括人为站点，抓地和火山口，表明所建议的方法通过不确定性分析成功地重建了无数据缺口，同时保留了原始NAC的高分辨率。 DEM。