Human exploration of the moon is expected to resume in the next decade, following the last such activities in the Apollo programme time. One of the major objectives of returning to the Moon is to continue retrieving geological samples, with a focus on collecting high-quality specimens to maximize scientific return. Tools that assist astronauts in making informed decisions about sample collection activities can maximize the scientific value of future lunar missions. A lunar rock classifier is a tool that can potentially provide the necessary information for astronauts to analyze lunar rock samples, allowing them to augment in-situ value identification of samples. Towards demonstrating the value of such a tool, in this paper, we introduce a framework for classifying rock types in thin sections of lunar rocks. We leverage the vast collection of petrographic thin-section images from the Apollo missions, captured under plane-polarized light (PPL), cross-polarised light (XPL), and reflected light at varying magnifications. Advanced machine learning methods, including contrastive learning, are applied to analyze these images and extract meaningful features. The contrastive learning approach fine-tunes a pre-trained Inception-Resnet-v2 network with the SimCLR loss function. The fine-tuned Inception-Resnet-v2 network can then extract essential features effectively from the thin-section images of Apollo rocks. A simple binary classifier is trained using transfer learning from the fine-tuned Inception-ResNet-v2 to 98.44\% ($\pm$1.47) accuracy in separating breccias from basalts.

翻译：在阿波罗计划最后一次此类活动之后，预计人类将在未来十年内恢复对月球的探索。重返月球的主要目标之一是继续获取地质样本，重点是收集高质量标本以最大化科学回报。协助宇航员就样本采集活动做出明智决策的工具，可以最大化未来月球任务的科学价值。月球岩石分类器是一种能够为宇航员分析月球岩石样本提供必要信息的工具，使其能够增强样本的原位价值识别。为论证此类工具的价值，本文提出了一种对月球岩石薄片进行岩性分类的框架。我们利用了阿波罗任务中获取的大量岩相薄片图像，这些图像是在单偏光（PPL）、正交偏光（XPL）和反射光下以不同放大倍数拍摄的。应用包括对比学习在内的先进机器学习方法来分析这些图像并提取有意义的特征。该对比学习方法使用SimCLR损失函数对预训练的Inception-Resnet-v2网络进行微调。微调后的Inception-Resnet-v2网络能够有效地从阿波罗岩石薄片图像中提取关键特征。通过从微调的Inception-ResNet-v2网络进行迁移学习，训练了一个简单的二元分类器，在区分角砾岩与玄武岩的任务中达到了98.44\% ($\pm$1.47)的准确率。