Spectral unmixing analysis for the lunar surface mineralogy using Chandrayaan-2 IIRS imagery

Abstract

Hyperspectral imagery (HSI) in the visible to near-infrared wavelength region has a high potential for deciphering mineral compositions of terrestrial and planetary surfaces. Thus, ISRO's Imaging Infrared Spectrometer (IIRS) sensor onboard Chandrayaan-2 (Ch-2) orbiter provides an opportunity to utilise the hyperspectral observations to characterise the lunar surface minerals and composition significantly. Hence, a sensitivity study of hyperspectral observations of the IIRS sensor was carried out for the detection and mapping of various lunar minerals using spectral unmixing analysis. A common region within the hyperspectral image obtained from Chandrayaan-1's (Ch-1) Moon Mineralogy Mapper (M3) sensor and Ch-2 IIRS sensor has been identified in the South-East of the Taurus-Littrow valley nearby Gardner crater for comparison and validation purposes. Ch-2 IIRS is an advanced hyperspectral imaging spectrometer capable of collecting spectra over a wider spectral range (800 ​nm ​− ​5000 ​nm) with high spatial (~80 m) and spectral resolution (20 n m ​− ​25 ​n m ). To examine spectral unmixing analysis, an electromagnetic range (800 ​nm ​− ​2500 ​nm) in the near-infrared region was chosen for the Ch-2 IIRS sensor. An L 1 ​− ​ norm based denoising algorithm is used for mixed noise removal from both the IIRS and M3 reflectance datasets. Endmember extraction and fractional abundance estimation are carried out using the N-Findr algorithm and the fully-constrained least square method, respectively. Our results reveal different minerals such as high-calcium pyroxene (Clinopyroxene), low-calcium pyroxene (Orthopyroxene), and regolith surface (matured and younger). The results obtained from the IIRS and M3 observations are in strong agreement. Hence, the hyperspectral observations made by the IIRS sensor are extremely effective for describing the mineral and chemical compositions of the lunar surface. • Spectral unmixing analysis is executed to determine lunar surface mineralogy and their abundance using Hyperspectral data. • For comparative analysis, co-located datasets from Chandrayaan-2 (Ch-2) IIRS and Chandrayaan-1(Ch-1) M3 are investigated. • An L1-norm-based denoising algorithm is introduced to significantly improve the data quality by removing the complex noise. • The methodology can be automated to develop the lunar mineralogical and chemical map in a time-efficient and systematic way.