A Novel Clustering-Based Methodology for Mapping Lunar Surface Minerals Using Moon Mineralogy Mapper (M³) Hyperspectral Data

In this study, we introduce a novel clustering-based methodology for mapping lunar surface mineralogy using hyperspectral data from the Moon Mineralogy Mapper (M³). The proposed methodology utilizes the Hapke photometric model to convert reflectance values to Single Scattering Albedo (SSA) values and constructs the distribution/histogram of the positions where the SSA signatures of the pixels in the entire image exhibit absorptions. Then, based on this histogram, an appropriate feature representation for each pixel is defined, oriented to the characterization of the pixel mineral composition. The k-means clustering algorithm is then applied on the new pixel representations. Mineral composition analysis for each cluster is then performed by evaluating whether the absorption features of each pixel match predefined absorption rules based on the spectral characteristics of eight selected typical minerals found on the Moon. Moreover, the proposed methodology provides mineral compositions within each pixel. The methodology is applied on an M³ dataset covering an area at the eastern Mare Serenitatis and the western Mare Tranquillitatis. The main results reveal spatial and compositional variations among clusters, and they are compatible with prior knowledge on various lunar maria basaltic compositions, demonstrating the reliability of the proposed methodology applied on planetary mineral exploration.