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Automatic Crater Detection Using Convex Grouping and Convolutional Neural Networks

  • Ebrahim Emami
  • George BebisEmail author
  • Ara Nefian
  • Terry Fong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Line Segment Convolutional Neural Network Lunar Reconnaissance Orbiter Small Crater Haar Feature 
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.

Notes

Acknowledgements

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

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ebrahim Emami
    • 1
  • George Bebis
    • 1
    Email author
  • Ara Nefian
    • 2
  • Terry Fong
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of NevadaRenoUSA
  2. 2.Intelligent Robotics Group (IRG)NASA Ames Research CenterMountain ViewUSA

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