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

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 detect...

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Published in:Remote Sensing
Main Authors: Yutong Jia, Lei Liu, Siqing Peng, Mingyang Feng, Gang Wan
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/rs14061391
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/6/1391/ 2023-08-20T04:08:38+02:00 An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery Yutong Jia Lei Liu Siqing Peng Mingyang Feng Gang Wan agris 2022-03-13 application/pdf https://doi.org/10.3390/rs14061391 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14061391 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 6; Pages: 1391 crater detection rilles detection space energy resources GL-HRNet transfer learning density distribution Text 2022 ftmdpi https://doi.org/10.3390/rs14061391 2023-08-01T04:26:48Z 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. Text North Pole MDPI Open Access Publishing North Pole Remote Sensing 14 6 1391
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic crater detection
rilles detection
space energy resources
GL-HRNet
transfer learning
density distribution
spellingShingle crater detection
rilles detection
space energy resources
GL-HRNet
transfer learning
density distribution
Yutong Jia
Lei Liu
Siqing Peng
Mingyang Feng
Gang Wan
An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
topic_facet crater detection
rilles detection
space energy resources
GL-HRNet
transfer learning
density distribution
description 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.
format Text
author Yutong Jia
Lei Liu
Siqing Peng
Mingyang Feng
Gang Wan
author_facet Yutong Jia
Lei Liu
Siqing Peng
Mingyang Feng
Gang Wan
author_sort Yutong Jia
title An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
title_short An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
title_full An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
title_fullStr An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
title_full_unstemmed An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
title_sort efficient high-resolution global–local network to detect lunar features for space energy discovery
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14061391
op_coverage agris
geographic North Pole
geographic_facet North Pole
genre North Pole
genre_facet North Pole
op_source Remote Sensing; Volume 14; Issue 6; Pages: 1391
op_relation Remote Sensing Image Processing
https://dx.doi.org/10.3390/rs14061391
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14061391
container_title Remote Sensing
container_volume 14
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container_start_page 1391
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