Texture based classification of geological materials using Deep Learning - Proof of concept for Planetary Surface Analysis

Recent studies in machine learning (ML) and geology have demonstrated a strong potential for automated classification of rocks and minerals. Though, the performance of ML models like Convolutional Neural Networks (CNNs), for pattern recognition of geological textures remains limited under controlled microscopic imaging conditions. This study explores the possibility of automated classification of multiple rocks and minerals including visually similar samples using microscopic texture information.

Initially, microscopic images of terrestrial basalt and magnetite which are visually similar under RGB microscopy, were captured using a digital USB microscope under varying illumination and magnification settings. These materials were selected to evaluate the performance of CNN models on differences in grain size, crystallinity and surface reflectance. A dataset comprising 2500 images per class was created and expanded using several augmentation techniques to increase the robustness of the model. With transfer learning, multiple models were trained amongst which InceptionV3 model achieved the highest validation accuracy for the initial binary classification problem.

The trained model achieved a validation accuracy of 98.30% and a test accuracy of 95%, demonstrating strong generalization capabilities. To assess the model’s effectiveness, performance metrics such as Precision, F1-Score, Confusion Matrix and ROC curve were examined. These findings provide insight into the strengths of CNN based pattern recognition in geological applications and demonstrate how deep learning techniques can be used for automated texture based classification.

Also, while this study does not directly utilize planetary datasets, it establishes a foundation for future applications of texture based ML methods in autonomous rover operations for geological analysis. We aim to extend this study to multiple basaltic variants and lithological classes under conditions relevant to Martian exploration, for building robust ML algorithms which can be used for geological image analysis.