Abstract
Abstract. In this study we apply a nonlinear spectral unmixing algorithm to a nearly global lunar spectral reflectance mosaic derived from hyper-spectral image data acquired by the Moon Mineralogy Mapper (M3) instrument. Corrections for topographic effects and for thermal emission were performed. A set of 19 laboratory-based reflectance spectra of lunar samples published by the Lunar Soil Characterization Consortium (LSCC) were used as a catalog of potential endmember spectra. For a given spectrum, the multi-population population-based incremental learning (MPBIL) algorithm was used to determine the subset of endmembers actually contained in it. However, as the MPBIL algorithm is computationally expensive, it cannot be applied to all pixels of the reflectance mosaic. Hence, the reflectance mosaic was clustered into a set of 64 prototype spectra, and the MPBIL algorithm was applied to each prototype spectrum. Each pixel of the mosaic was assigned to the most similar prototype, and the set of endmembers previously determined for that prototype was used for pixel-wise nonlinear spectral unmixing using the Hapke model, implemented as linear unmixing of the single-scattering albedo spectrum. This procedure yields maps of the fractional abundances of the 19 endmembers. Based on the known modal abundances of a variety of mineral species in the LSCC samples, a conversion from endmember abundances to mineral abundances was performed. We present maps of the fractional abundances of plagioclase, pyroxene and olivine and compare our results with previously published lunar mineral abundance maps.
Highlights
Systematic analyses of the minerals that constitute the lunar surface material based on orbital multispectral and hyperspectral images of the Moon have been performed since the Clementine mission in 1994 (Nozette et al, 1994)
Global lunar maps of the abundances of the elements Fe and Ti have been constructed based on Clementine UV/VIS and Kaguya Multiband Imager (MI) multispectral imagery, where the calibration was performed based on laboratory data derived from returned lunar samples (Lucey et al, 2000, LeMouelic et al, 2000, Otake et al, 2012)
In this paper we have described an approach to endmember selection for spectral unxmixing that relies on multi-population population-based incremental learning (MPBIL)
Summary
Systematic analyses of the minerals that constitute the lunar surface material based on orbital multispectral and hyperspectral images of the Moon have been performed since the Clementine mission in 1994 (Nozette et al, 1994). Elemental abundances of the elements Ca, Al, Fe, Mg, Ti and O have been mapped by (Shkuratov et al, 2005) using a regression between Clementine UV/VIS multispectral data and low-resolution Lunar Prospector Gamma Ray Spectrometer (LP GRS) elemental abundance data (Lawrence e al., 1998) These methods rely on the absorption band near 1 μm wavelength which is due to the presence of mafic minerals such as pyroxene or olivine. Maps of the distribution of lunar key minerals have been constructed by (Lucey, 2004) based on Clementine UV/VIS spectral reflectance data, relying on a database that contains about 85000 spectra of mixtures of varying mineral and elemental composition, regolith grain size and degree of spaceweathering, which were computed using the Hapke model. Based on the obtained spectral unmixing results, nearly global maps of the fractional abundances of key lunar minerals are constructed
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