论文标题
一种神经过程方法，用于在月球数字图中重建NO-DATA差距的概率重建
A Neural Process Approach for Probabilistic Reconstruction of No-Data Gaps in Lunar Digital Elevation Maps
论文作者
论文摘要
随着NASA的月球侦察轨道（LRO）的出现，通过使用窄角摄像头（NACS）来构建大量的高分辨率数字高程图（DEMS）来表征月球表面。但是，NAC DEMS通常包含NO-DATA间隙（voids），这使得地图降低了可靠。为了解决该问题，本文为NAC Dems中NO-DATA差距的概率重建提供了一个基于学习的框架。该框架建立在最先进的随机过程模型，细心的神经过程（ANP）上，并预测目标坐标（纬度和经度）在附近地区观察到的海拔数据上的条件分布。此外，本文提出了稀疏的细心神经过程（SANP），不仅将ANP O（n）的线性计算复杂性降低到恒定的复杂性O（k），还可以通过防止过度拟合和过度光滑的问题来增强重建性能。在Apollo 17着陆点（20.0°N和30.4°E）上评估了所提出的方法，这表明建议的方法成功地通过不确定性分析重建了NO-DATA差距，同时保留了原始NAC DEM的高分辨率。
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 reduces the linear computational complexity of the ANP O(N) to the constant complexity O(K) but enhance the reconstruction performance by preventing overfitting and over-smoothing problems. The proposed method is evaluated on the Apollo 17 landing site (20.0°N and 30.4°E), demonstrating that the suggested approach successfully reconstructs no-data gaps with uncertainty analysis while preserving the high resolution of original NAC DEMs.
