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International Symposium on Visual Computing

ISVC 2015: Advances in Visual Computing pp 213–224Cite as

Automatic Crater Detection Using Convex Grouping and Convolutional Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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.

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Acknowledgements

This material is based upon work supported by NASA EPSCoR under cooperative agreement No. NNX11AM09A.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    Ebrahim Emami & George Bebis

  2. Intelligent Robotics Group (IRG), NASA Ames Research Center, Mountain View, USA

    Ara Nefian & Terry Fong

Authors
  1. Ebrahim Emami
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  2. George Bebis
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  3. Ara Nefian
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  4. Terry Fong
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Corresponding author

Correspondence to George Bebis .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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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

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  • 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)

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