Lunar Image Classification for Terrain Detection

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 ch...

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Bibliographic Details
Published inAdvances in Visual Computing pp. 1 - 8
Main Authors Cheng, Heng-Tze, Sun, Feng-Tso, Buthpitiya, Senaka, Zhang, Ying, Nefian, Ara V.
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Decision Tree Classifier
Gradient Orientation
Image Patch
Machine Translation
Terrain Type
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Summary: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.
ISBN:9783642172762
3642172768
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-17277-9_1