Abstract
Craters are some the most important landmarks on the surface of many planets which can be used for autonomous safe landing and spacecraft and rover navigation. Manual detection of craters is laborious and impractical, and many approaches have been proposed in the field to automate this task. However, none of these methods have yet become a standard tool for crater detection due to the challenging nature of this problem. In this paper, we propose a new crater detection algorithm (CDA) which employs a multi-scale candidate region detection step based on convexity cues and candidate region verification based on machine learning. Using an extensive dataset, our method has achieved a 92 % detection rate with an 85 % precision rate.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bandeiraa, L., Ding, W., Tomasz, F.: Detection of sub-kilometer craters in high resolution planetary images using shape and texture features. Adv. Space Res. 49(1), 64–74 (2012)
Yu, Z., Zhu, S., Cui, P.: Sequence detection of planetary surface craters from DEM data. In: World Congress on Intelligent Control and Automation (2012)
Maoyin, A., Pan, W.: Crater Detection algorithm with part PHOG features for safe landing. In: International Conference on Systems and Informatics, pp. 103–106 (2012)
Salamunićcara, G., Lončarićb, S., Mazarico, E.: LU60645GT and MA132843GT catalogues of lunar and martian impact craters developed using a crater shape-based interpolation crater detection algorithm for topography data. Planet. Space Sci. 60(1), 236–247 (2012)
Kamarudin, N., Ghani, N., Mustapha, M., Ismail, A., Daud, N.: An overview of crater analyses, tests and various methods of crater detection algorithm. Front. Environ. Eng. 1(1), 1–7 (2012)
Salamunićcara, G., Lončarić, S.: Open framework for objective evaluation of crater detection algorithms with first test-field subsystem based on MOLA data. Adv. Space Res. 42(1), 6–19 (2008)
Smirnov, A.: Exploratory Study of Automated Crater Detection (2012)
Troglio, G., Le Moigne, J., Benediktsson, A., Moser, G., Serpico, S.: Automatic extraction of ellipsoidal features for planetary image registration. Geosci. Remote Sens. Lett. 9(1), 95–99 (2012)
Kim, J., Muller, J.: Impact Crater Detection on Optical Images and DEMS, International Society for Photogrammetry and Remote Sensing, Working Group IV/9: Extraterrestrial Mapping Workshop, Advances in Planetary Mapping (2003)
Ding, M., Caob, Y., Wub, Q.: Novel approach of crater detection by crater candidate region selection and matrix-pattern-oriented least squares support vector machine. Chin. J. Aeronaut. 26(2), 385–389 (2013)
Martins, R., Pina, P., Marques, J., Silveira, M., Silveira, M.: Crater detection by a boosting approach. Geosci. Remote Sens. Lett. 6(1), 127–131 (2009)
Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Radu, V.: Application. In: Radu, V. (ed.) Stochastic Modeling of Thermal Fatigue Crack Growth. ACM, vol. 1, pp. 178–184. Springer, Heidelberg (2015)
Palafox, L., Alvarez, A., Hamilton, C.: Automated Detection of impact craters and volcanic rootless cones in mars satellite imagery using convolutional neural networks and support vector machines. In: 46th Lunar and Planetary Science Conference (2015)
Salamuniccar, G., Loncaric, S.: Method for crater detection from martian digital topography data using gradient value/orientation, morphometry, vote analysis, slip tuning, and calibration. IEEE Trans. Geosci. Remote Sens. 48(5), 2317–2329 (2010)
Xie, Y., Tang, G., Yan, S., Hui, L.: Crater detection using the morphological characteristics of Chang’E-1 digital elevation models. Geosci. Remote Sens. Lett. IEEE 10(4), 885–889 (2013)
Jacobs, D.: Robust and efficient detection of convex groups. IEEE Trans. Patern Anal. Mach. Intell. 18(1), 23–37 (1996)
Pavlidis, T., Horowitz, S.: Segmentation of plane curves. IEEE Trans. Comput. C-23(8), 860–870 (1974)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)
Acknowledgements
This material is based upon work supported by NASA EPSCoR under cooperative agreement No. NNX11AM09A.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Emami, E., Bebis, G., Nefian, A., Fong, T. (2015). Automatic Crater Detection Using Convex Grouping and Convolutional Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-27863-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27862-9
Online ISBN: 978-3-319-27863-6
eBook Packages: Computer ScienceComputer Science (R0)