论文标题
农历表面异常检测的无监督分布学习
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
论文作者
论文摘要
在这项工作中，我们表明，现代数据驱动的机器学习技术可以成功地应用于月球表面遥感数据，以无监督的方式学习数据分布的足够良好表示，以实现月球技术和异常检测。特别是，我们训练一个无监督的分布学习神经网络模型，以在测试数据集中找到Apollo 15登录模块，而没有数据集特定模型或超参数调整。足够良好的无监督数据密度估计有望实现无数有用的下游任务，包括在未来的太空飞行和殖民地进行登录，找到新的影响陨石坑或月球表面重塑，并在算法上确定不受伤残的样品的重要性，从而从电力和带网构成的符合人数和乐队构成的符合人数的重要性。我们在这项工作中表明，这种无监督的学习可以在月球遥感和太空科学环境中成功完成。
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. In particular we train an unsupervised distribution learning neural network model to find the Apollo 15 landing module in a testing dataset, with no dataset specific model or hyperparameter tuning. Sufficiently good unsupervised data density estimation has the promise of enabling myriad useful downstream tasks, including locating lunar resources for future space flight and colonization, finding new impact craters or lunar surface reshaping, and algorithmically deciding the importance of unlabeled samples to send back from power- and bandwidth-constrained missions. We show in this work that such unsupervised learning can be successfully done in the lunar remote sensing and space science contexts.
