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Proceedings Paper

Classification of lunar soil from reflectance spectrum by PCA and SVM
Author(s): Xiaoyu Zhang; Maohai Huang; Jun Chu; Chunlai Li
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Paper Abstract

Scientists on the ground need understand the environment around the unmanned lunar rover in lunar exploration through analyzing data obtained by various payloads. There are two main material on the moon, high land material and mare material on the moon. We use reflectance spectrums of lunar soils from Apollo mission measured by LSCC to classify the two kinds of materials. Principal component analysis is applied to reduce and select the feature of the reflectance spectrums. These features input support vector machine, which base on statistical learning theory and is used widely to classify in modern pattern recognition. Our work shows that the reflectance spectrums of lunar soils are strong link with the material which they represent.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678817 (15 November 2007); doi: 10.1117/12.749204
Show Author Affiliations
Xiaoyu Zhang, National Astronomical Observatories (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Maohai Huang, National Astronomical Observatories (China)
Jun Chu, National Astronomical Observatories (China)
Nanchang Univ. of Aeronautics (China)
Chunlai Li, National Astronomical Observatories (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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