A neural process approach for probabilistic reconstruction of no-data gaps in lunar digital elevation maps

Abstract

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 O(N) to the constant complexity 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. (C) 2021 Elsevier Masson SAS. All rights reserved.