Research On Spectrum Analysis And Mineral Inversion Model Of Lunar Soil Simulants Based On AOTF Imaging Spectrometer

The exploration of the lunar surface material is an important part of lunar exploration. Information inversion of the lunar soil hyperspectral data has vital significance to the lunar exploration, lunar resource development and researches on lunar geological evolution. Recently, using spectral data obtained by high resolution imaging spectrometer to realize the inversion of lunar information becomes a hot topic with a new round of lunar exploration. In this paper, we use infrared spectrometer developed by the Shanghai institute of Technical Physics of the Chinese Academy of Sciences (SITP) and ASD spectrometer to gather the hyperspectral data of lunar soil simulants. Basing on these data, the research of data preprocess and lunar mineral contents inversion is carried out. The main contents of this paper are listed as follow:(1) The data preprocess algorithms are improved for the spectral data of lunar soil simulants. Basing on the imaging principle and image features of infrared spectrometer developed by SITP, data process like noise removal, effective information extraction, target radiance inversion and relative reflectance calculation is carried on. In addition, on the basis of others’studies, the algorithm for data process has been improved and the spectral overlapping part between visible and near infrared channel and shortwave infrared channel has been integrated. As a result, the full-band relative reflectance curve of mineral samples can be achieved.(2) Spectral characteristic parameters extraction for lunar soil simulants. On the basis of the reflection and absorption characteristics of Pyroxene, Peridot, Plagioclase and Ilmenite, the spectral characteristic parameters that has greater correlation with mineral contents is extracted to establish inversion model.(3) Research on the inversion method and inversion model of the lunar mineral spectral data. According to the spectral characteristic parameters extracted in this article, using the method of Multiple Linear Regression, Partial Least Squares regression and Mixed Spectral Decomposition to establish inversion model to predict the content of Pyroxene, Peridot, Plagioclase and Ilmenitein mixed mineral samples.(4) Evaluation and analysis of the inversion results. Do comparison of the predicted contents and the real contents of each mineral in mixed mineral samples. Evaluate the inversion results and merits and demerits of the inversion model.