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.
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Cheng, HT., Sun, FT., Buthpitiya, S., Zhang, Y., Nefian, A.V. (2010). Lunar Image Classification for Terrain Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_1
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DOI: https://doi.org/10.1007/978-3-642-17277-9_1
Publisher Name: Springer, Berlin, Heidelberg
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