Automatic Crater Detection Using Convex Grouping and Convolutional Neural Networks
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.
KeywordsLine Segment Convolutional Neural Network Lunar Reconnaissance Orbiter Small Crater Haar Feature
This material is based upon work supported by NASA EPSCoR under cooperative agreement No. NNX11AM09A.
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