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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