Automated Lunar Surface Image Classification Using Deep Convolutional Neural Networks for Geological Feature Detection

Abstract

Robotic rovers have vastly expanded our understanding of the lunar surface, providing detailed imagery crucial for scientific research and future exploration. However, manually classifying this imagery is time-consuming and prone to errors, necessitating automated solutions. Automated classification of lunar surface imagery is vital for efficient data analysis, site selection for future missions, and advancing lunar exploration. Developing accurate and efficient image classification systems tailored for lunar terrain is thus imperative. The objective of this study is to develop and assess an image classification system utilizing Deep Convolutional Neural Networks (DCNNs) specifically for lunar surface images. The aim is to achieve high accuracy and efficiency in identifying geological features such as craters and dunes, as observed by robotic rovers. A curated dataset of lunar surface images was partitioned into training, testing, and validation subsets. DCNNs models were trained on the training dataset and evaluated using testing and validation datasets. Hyperparameter tuning and optimization techniques were employed to enhance model performance. The classification system based on DCNNs showed promising outcomes. Model B and F achieved an accuracy of 91.1%, while Model A and D achieved 87.5%. Model C attained an accuracy of 89.3%, and Model E reached 83.9%. Visualizations of training and validation metrics revealed distinct performance patterns across models, highlighting the potential for further advancements in lunar exploration research.