# Breccia and basalt classification of thin sections of Apollo rocks with deep learning

arXiv: https://arxiv.org/abs/2410.21024 · HTML: https://mlsanity.com/paper/2410.21024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey](https://mlsanity.com/paper/2001.04074/) (2020)
- [Rock Classification in Petrographic Thin Section Images Based on Concatenated Convolutional Neural Networks](https://mlsanity.com/paper/2003.10437/) (2020)

## Abstract (arXiv)

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% (±1.47) accuracy in separating breccias from basalts.