Abstract
Because of the impending energy crisis and the environmental Impact of fossil fuels, researchers are actively looking for alternatives, such as Helium-3 on the Moon. Although it remains challenging to explore energies on the Moon due to the long physical distance, the lunar features, such as craters and rilles, can be the hotspots for such energy sources, according to recent studies. Thus, identifying lunar features, such as craters and rilles, can facilitate the discovery of Helium-3 on the Moon, which is enriched in such hotspots. However, previously, no computational method was developed to recognize the lunar features automatically for facilitating space energy discovery. In our research, we aim at developing the first deep learning method to identify multiple lunar features simultaneously for potential energy source discovery. Based on the state-of-the-art deep learning model, High Resolution Net, our model can efficiently extract semantic information and high-resolution spatial information from the input images, which ensures the performance for recognizing the lunar features. With a novel framework, our method can recognize multiple lunar features, such as craters and rilles, at the same time. We also used transfer learning to handle the data deficiency issue. With comprehensive experiments on three datasets, we show the effectiveness of the proposed method. All the datasets and codes are available online. The source code and the data are available at https://github.com/frankchen121212/High-Resolution-MoonNet