Date of Original Version
Abstract or Description
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.
Computer and Systems Architecture Commons, Data Storage Systems Commons, Digital Circuits Commons, Digital Communications and Networking Commons, Hardware Systems Commons, Other Computer Engineering Commons, Other Electrical and Computer Engineering Commons, Robotics Commons, Systems and Communications Commons, VLSI and Circuits, Embedded and Hardware Systems Commons