A review on deep learning-based automated lunar crater detection

• Published in: Earth science informatics 2024-10, Vol.17 (5), p.3863-3898
• Main Authors: Chaini, Chinmayee, Jha, Vijay Kumar
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Classification; Deep learning; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Geological history; Image processing; Image segmentation; Information Systems Applications (incl.Internet); Lunar craters; Lunar surface; Machine learning; Object recognition; Ontology; Review; Semantic segmentation; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-024-01396-2

The lunar surface, which has been extensively explored and studied, offers valuable insights into its geological history and crater distribution due to the abundance of impact craters on its surface. Detecting numerous craters of different sizes on the lunar surface necessitated an automated process to avoid manual intervention, which consumed significant time and effort. However, traditional methods rely on manual feature extraction methods, encountering similar challenges, including low performance, particularly when confronted with diverse crater sizes and illumination conditions. In recent years, intelligent algorithms that introduce automated crater detection algorithms (CDAs) using deep learning (DL) techniques have played a vital role in detecting various sizes of craters on the lunar surface that may be missed or miss-classification by visual interpretation. This study outlines the challenges faced by traditional methods and explores recent advancements in DL techniques. The main objective is to provide a comprehensive review of prior studies, highlighting the advantages and limitations of each DL-based technique for automatic crater detection. Additionally, this study aggregates existing research on various image-processing tasks (such as semantic segmentation, classification-based, and object detection) utilizing DL-based techniques for detecting various sizes of craters on the lunar surface. Further, this study provides a comprehensive analysis of both manually and automatically compiled crater databases to assist new researchers in validating their models both qualitatively and quantitatively. By reviewing existing literature, this study aids new researchers in understanding the limitations and key findings of recent research, thereby promoting progress toward greater automation in crater detection.