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Modeling the Chandrayaan-1 Hyperspectral (HySI) Data for Mineral Mixing Analysis

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Soft Computing and Signal Processing (ICSCSP 2019)

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Abstract

The study of the lunar surface mineralogy is significant to understand the origin and its evolution. For the first time, an attempt was made to model the Chandrayaan-1 Hyperspectral (HySI) data using Hapke radiative transfer model for quantifying lunar minerals along with associated parameters like grain size, porosity and submicroscopic iron SMFe which is considered to be the main product of space weathering. The model spectra were created with selected pure end member from the RELAB database, and the model validation was done against the four standard mixtures. In all four test cases, the model reproduces the trends successfully. The RMSE and the correlation coefficient were calculated between measured and modeled spectra. After testing, the active spectral signatures from bright fresh craters derived from the data set covering the Mare Crisium were modeled. The model result shows the fresh craters from the Mare areas rich in Clinopyroxene with low agglutinates. The model gives around 10% of olivine. The spectra from highland give a high percentage of plagioclase and agglutinate. However, modeling the spectra from relatively mature soil with no significant absorption is difficult to model.

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Acknowledgements

The author is thankful for the financial assistance received from Department of Space (DoS, ISRO/SSPO/Ch-1/2016-17, August 17, 2016). This work is a part of the ISRO project under Chandrayaan-1 Announcement of Opportunity (AO) program. The research is based (partially or to a significant extent) on the results obtained from the Chandrayaan-1, first lunar mission of the ISRO, archived at the Indian Space Science Data Center (ISSDC).

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Mohammed Zeeshan, R., Sayyad Shafiyoddin, B. (2020). Modeling the Chandrayaan-1 Hyperspectral (HySI) Data for Mineral Mixing Analysis. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_44

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