MFBTFF-Net: A Novel Multi-Frequency Brightness Temperature Feature Fusion Network for Global Lunar Surface Oxides Abundance Estimation With Chang'e-2 Lunar Microwave Sounder Data - Yu Li; Zifeng Yuan; Sarah Mazhar; Zhiguo Meng; Yuanzhi Zhang; Jinsong Ping; Ferdinando Nunziata (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Research on lunar oxides abundance has been spotlighted for its great significance in reconstructing the evolutionary history of the moon. In recent years, artificial intelligence technologies have been introduced to map oxides abundance on the lunar surface for their reliability and robustness. However, there are still some shortcomings in existing studies. First, the majority of these studies rely on spectral data and used in situ (drilled) ground truth samples collected by satellite missions. The detection depth of spectral sensors and the drilled depths of the returned samples are not consistent, lowering the reliability of the results. Moreover, existing machine/deep learning models may not be suitable for processing the data acquired in lunar exploration. In this article, we propose a novel deep learning model named multifrequency brightness temperature feature fusion network (MFBTFF-Net) for processing Chang'e-2 lunar microwave sounder (CELMS) data and it exploits the thermal radiation features related to various drilling depths to acquire the global lunar oxide abundance maps. The experimental results demonstrated that the proposed MFBTFF-Net model can significantly improve the estimation precision of most lunar oxides. The proposed method achieved root-mean-square error indices of 1.4449, 1.4826, and 0.9824 (wt.%) on estimating Al2O3, FeO, and TiO2, which outperformed the state-of-the-art models by at least 0.0674, 0.6217, and 0.0578, respectively. Furthermore, based on the proposed model, we generated a new set of lunar oxide abundance maps. Compared with the abundance maps derived from spectral data, some discoveries can be obtained due to the unique penetration depth-related information provided by Chang'e-2 CELMS data. This study demonstrates the large potential of Chang'e-2 CELMS as a powerful new tool to understand the vertical structures of the moon under the regolith.