用机器学习在月球影像中自动发现异常地貌和人造物。
A Machine Learning Based Search for Lunar Anomalies
- 用变分自编码器无监督检测月表异常特征。
- 成功定位普拉斯基特陨石坑、帕拉塞尔苏斯C陨石坑等目标。
- 适合月球地质研究与深空探测任务中的异常发现。
自2009年以来，月球勘测轨道器（LRO）利用窄角相机以每像素0.5至2米的分辨率持续采集月球高分辨率图像，形成了大规模数据集，为月表研究提供了前所未有的规模支持。本文旨在测试Lesnikowski等人（2024）提出的贝塔变分自编码器（Beta-VAE）模型的能力，该模型可无监督识别月表异常特征，不仅包括岩屑堆积、新撞击坑、不规则月海斑块、火山坑或熔岩管坍塌等地质构造，还包括着陆航天器等人工物体。实验验证了该模型在统计显著水平上成功找回两个重要地点（普拉斯基特陨石坑与帕拉塞尔苏斯C陨石坑）以及多个已知技术着陆体。
原文摘要 · Abstract (English)
The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale. Here, we aim to test the abilities of the Beta-Variational Autoencoder (VAE) created by Lesnikowski et al. (2024), an unsupervised learning model which identifies anomalous features across the Moon's surface, locating not only scientifically useful geologic formations such as rockfall deposits, fresh impact craters, irregular mare patches, or volcanic pits/collapsed lava tubes, but also artificial objects such as landed spacecraft. This investigation further gauged the model's ability to locate anomalous surface features, successfully recovering two places of interest (Plaskett Crater and Paracelsus C Crater) and numerous landed technological assets at a statistically significant rate.
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