An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

Yutong Jia,Gang Wan,Mingyang Feng,Lei Liu,Siqing Peng

doi:10.3390/rs14061391

Abstract

Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research.

Highlights

By combining deep learning and transfer learning, we proposed the GL-high-resolution net (HRNet) for lunar energy detection based on GLNet [24], HRNet [25], and UNet [26], as shown in Firstly, projection, downsampling, and random clipping were carried out for different remote-sensing data to make complex data fragmentary
The GL-HRNet combined the advantages of ResUnet and HRNet networks, which can detect smaller impact craters and perform better for overlapping impact craters, closely related to the aggregation method
1) The difference boundary of impact crater density is highly consistent with the boundfeature detection method (GL-HRNet) combining high-resolution features and improved ary between lunar mare and highlands; GLNet was used to further promote lunar energy discovery and geological research

Summary

Introduction

Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. The mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. By exploring rilles and impact craters on the Moon, we are more likely to find space energy because they have been a hot research topic [3]. The study of impact craters can be used to deduce geological age, to explore the existence of water ice [7], and to select landing sites for lunar rovers [8], autonomous navigation [9], and other tasks for deep space probes [10]

Methods

Results

Conclusion