Quantification of the chemical composition of lunar soil in terms of its reflectance spectra by PCA and SVM
In the second phase of the Chang'E Program an unmanned lunar rover will be launched onto the Moon. When ground scientists get a full understanding of the chemical composition of lunar soil around the rover, they can make more detailed survey plans for the rover and various payloads onboard so as to satisfy their scientific objectives. There is an obvious relationship between the reflectance of lunar soil and its chemical characteristics. Both principal component analysis (PCA) and support vector machine (SVM) models were applied to establishing the relationship between the reflectance spectra and chemical compositions of lunar highland and mare soil samples sent back by Apollo missions 11, 12, 14, 15, 16 and 17 and measured by Lunar Soil Characterization Consortium (LSCC). PCA was used to reduce and select the features of the reflectance spectra of lunar soil samples. Then, these features were put into SVM to estimate the abundances of various chemical components in lunar soil. We also compared the results of our measurement with those obtained by the SVM model [partial least squares (PLS)] and the principal component regression (PCR) model reported in literature. Our studies showed that with the exception of TiO2, the results of prediction of the abundances of chemical compounds in lunar soil by our model are much more reliable than those reported in literature. The reflectance spectra of lunar soil are closely related to the materials from which it was derived.
作 者： ZHANG Xiaoyu LI Chunlai LU Chang 作者单位： ZHANG Xiaoyu(Graduate University of Chinese Academy of Sciences, Beijing 100049, China;National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China)LI Chunlai,LU Chang(National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China)
刊 名： 中国地球化学学报（英文版） 英文刊名： CHINESE JOURNAL OF GEOCHEMISTRY 年，卷(期)： 2009 28(2) 分类号： P59 关键词： lunar exploration lunar soil reflectance spectrum pattern recognition PCA SVM chemical composition abundance