Signal Compensation of Moon Mineralogy Mapper (M3) Under Low-Illumination Conditions Using a CycleGAN-Based Network

Lunar south polar regions have attracted considerable scientific interest due to their potential for preservation of water ice and unique mineralogical compositions. As a key scientific payload for surface composition exploration missions, hyperspectral imager faces significant challenges in the lunar polar regions. The primary issue is the low-illumination conditions in these areas, where terrain-induced shadows drastically reduce the signal-to-noise ratio (SNR) of hyperspectral images (HSIs), resulting in limited availability of reliable spectral available for polar region analysis. Previous studies have largely bypassed low-SNR spectra or filtered them out, as there has been no effective method to recover the spectral information under these harsh conditions. To tackle this problem, an effective method based on CycleGAN network is proposed to compensate hyperspectral data obtained by Moon mineralogy mapper (M3) under low-illumination conditions in lunar south polar regions. The network was trained by constructing paired datasets of low and high SNR M3 spectra from the lunar South Pole. The efficacy of the proposed method is validated using real high SNR M3 spectral observations, with the performance of the compensated results comprehensively assessed across three dimensions: structural indicators, spectral indices, and spatial consistency analysis. The strong correlation between the M3 spectral compensation results with Selenological Engineering Explorer (Kaguya) multiband imager data, as well as other sensors' inversion of plagioclase abundance around the Shackleton Crater, underscores the network's potential for mineral exploration. To the best of authors' knowledge, this study represents one of the first efforts to compensate illumination-limited spectra in lunar HSI. It provides an efficient method for enhancing the SNR of M3 spectra in the lunar polar region, offering a reliable tool and novel insights for future mineralogical and water ice studies.