Classification of lunar soil from reflectance spectrum by PCA and SVM

Xiaoyu Zhang; Maohai Huang; Jun Chu; Chunlai Li

doi:10.1117/12.749204

15 November 2007 Classification of lunar soil from reflectance spectrum by PCA and SVM

Xiaoyu Zhang, Maohai Huang, Jun Chu, Chunlai Li

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 678817 (2007) https://doi.org/10.1117/12.749204
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Xiaoyu Zhang, Maohai Huang, Jun Chu, and Chunlai Li "Classification of lunar soil from reflectance spectrum by PCA and SVM", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678817 (15 November 2007); https://doi.org/10.1117/12.749204

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