Crater detection using unsupervised algorithms and convolutional neural networks
Abstract
Craters are among the most abundant features on the surface of many planets with great importance for planetary scientists. They reveal chronology information about planets and may be used for autonomous spacecraft navigation and landing. Although numerous research efforts have been carried out in the field of crater detection, existing crater detection algorithms (CDAs) are only helpful in a limited number of applications. A promising crater detection approach involves two main steps: 1) hypothesis generation (HG) and 2) hypothesis verification (HV). During HG, potential crater locations are detected. The validity of the hypothesized crater locations is then tested in a HV step. In this context, we discuss some commonly used algorithms for HG such as highlight-shadow region detection and Hough transform as well as our novel and enhanced algorithms based on interest point detection and convex grouping. A key objective of this paper is to analyze their performance while paying special attention to how they affect the accuracy of the verification step. To deal with different size craters, we focus on multiscale HG. For HV, we have chosen convolutional neural networks which have recently achieved state-of-the-art performance in many computer vision applications. Due to the variation of test sets in the literature, it is often challenging to compare the performance of different CDAs in a fair way. In this paper, we present a comprehensive performance evaluation and comparison of CDAs. Each algorithm has been trained/tested using common data sets generated by a systematic approach.
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State-of-the-art crater detectors make use of Object Detection Networks and image segmentation.
“State of the art detectors make use of Object Detection Networks (ODN) [18,19] image segmentation [20,21,22].”
From Optical navigation for Lunar landing based on Convolutional Neural Network crater detector (2022) · cites this paper as [19]
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Optical navigation for Lunar landing based on Convolutional Neural Network crater detector
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- Authors
- Emami, E., Ahmad, T., Bebis, G., Nefian, A., & Fong, T.
- Affiliations
- University of Nevada, Reno (US)
- Ames Research Center (US)
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2019-08
- Volume
- 57
- Issue
- 8
- Pages
- 5373-5383
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2922359028
- Cited by
- 48
- References
- 35
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 11 · 2023: 1 · 2022: 9 · 2021: 6 · 2020: 3
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
- Funders
- National Aeronautics and Space Administration (NNX11AM09A)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Planetary Science and Exploration
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- crater detection, planetary surfaces, hypothesis generation, hypothesis verification, keypoint detection, Hough transform, convolutional neural networks, algorithm performance evaluation
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