Journal
ADVANCES IN SPACE RESEARCH
Volume 64, Issue 8, Pages 1615-1628Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2019.07.017
Keywords
Crater detection; Feature extraction; Automation; Machine learning; Convolutional neural networks; Mars
Categories
Funding
- School of Engineering, University of Tokyo (SEUT)
- JAXA International Top Young Fellowship
Ask authors/readers for more resources
Convolutional Neural Networks (CNN) offer promising opportunities to automatically glean scientifically relevant information directly from annotated images, without needing to handcraft features for detection. Crater counting started with hand counting hundreds, thousands, or even millions of craters in order to determine the age of geological units on planetary bodies of the solar system. Automated crater detection algorithms have attempted to speed up this process. Previous research has employed computer vision techniques with handcrafted features such as light and shadow patterns, circle finding, or edge detection. This research continues, but now some researchers use techniques like convolutional neural networks that enable the algorithm to develop its own features. As the field of machine learning undergoes exponential growth in terms of paper count and research methods, the crater counting application can benefit from the new research, especially when conducting joint interdisciplinary projects. Despite these advancements, the crater counting community has not yet adopted standard methods for automating the process despite decades of research. This survey enumerates challenges for both planetary geologists and machine learning researchers, looks at the recent automatic crater detection advancements using machine learning techniques (primarily in methods using CNNs), and makes recommendations for the path toward greater automation. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available