Classification of lunar soil from reflectance spectrum by PCA and SVM

Details Classification of lunar soil from reflectance spectrum by PCA and SVM Classification of lunar soil from reflectance spectrum by PCA and SVM

Nov 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007spie.6788e..42z&link_type=abstract

MIPPR 2007: Pattern Recognition and Computer Vision. Edited by Maybank, S. J.; Ding, Mingyue; Wahl, F.; Zhu, Yaoting. Proceedin

Computer Science

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Scientific paper

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.

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