Lunar image classification for terrain detection

Authors:
Heng-Tze Cheng;Feng-Tso Sun;Senaka Buthpitiya;Ying Zhang;Ara V. Nefian
Affiliations:
Department of Electrical and Computer Engineering, Carnegie Mellon University;Department of Electrical and Computer Engineering, Carnegie Mellon University;Department of Electrical and Computer Engineering, Carnegie Mellon University;Department of Electrical and Computer Engineering, Carnegie Mellon University;NASA Ames Research Center
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Year:
2010

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Abstract

Terrain detection and classification are critical elements for NASA mission preparations and landing site selection. In this paper, we have investigated several image features and classifiers for lunar terrain classification. The proposed histogram of gradient orientation effectively discerns the characteristics of various terrain types. We further develop an open-source Lunar Image Labeling Toolkit to facilitate future research in planetary science. Experimental results show that the proposed system achieves 95% accuracy of classification evaluated on a dataset of 931 lunar image patches from NASA Apollo missions.