Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images
Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China’s Chang’e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data ca...
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ftdoajarticles:oai:doaj.org/article:6839eef0764f41c09b694d370feff5fc 2024-09-09T20:08:57+00:00 Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images Tong Xia Xuancheng Ren Yuntian Liu Niutao Liu Feng Xu Ya-Qiu Jin 2024-05-01T00:00:00Z https://doi.org/10.3390/rs16111834 https://doaj.org/article/6839eef0764f41c09b694d370feff5fc EN eng MDPI AG https://www.mdpi.com/2072-4292/16/11/1834 https://doaj.org/toc/2072-4292 doi:10.3390/rs16111834 2072-4292 https://doaj.org/article/6839eef0764f41c09b694d370feff5fc Remote Sensing, Vol 16, Iss 11, p 1834 (2024) crater detection rocks lunar south pole PSR SAR Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16111834 2024-08-05T17:49:12Z Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China’s Chang’e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data can hardly detect surface rocks because of median-averaging. High-resolution images from a synthetic aperture radar (SAR) can be used to map discrete rocks and small craters according to their strong backscattering. This study utilizes the You Only Look Once version 7 (YOLOv7) tool to detect varying-sized craters in SAR images. It also employs the Markov random field (MRF) algorithm to identify surface rocks, which are usually difficult to detect in DEM data. The results are validated by optical images and DEM data in non-PSR. With the assistance of the DEM data, regions with slopes larger than 10° are excluded. YOLOv7 and MRF are applied to detect craters and rocky surfaces and exclude regions with steep slopes in the PSRs of craters Shoemaker, Slater, and Shackleton, respectively. This study proves SAR images are feasible in the selection of landing sites in the PSRs for future missions. Article in Journal/Newspaper South pole Directory of Open Access Journals: DOAJ Articles Shackleton South Pole Remote Sensing 16 11 1834 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
crater detection rocks lunar south pole PSR SAR Science Q |
spellingShingle |
crater detection rocks lunar south pole PSR SAR Science Q Tong Xia Xuancheng Ren Yuntian Liu Niutao Liu Feng Xu Ya-Qiu Jin Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images |
topic_facet |
crater detection rocks lunar south pole PSR SAR Science Q |
description |
Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China’s Chang’e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data can hardly detect surface rocks because of median-averaging. High-resolution images from a synthetic aperture radar (SAR) can be used to map discrete rocks and small craters according to their strong backscattering. This study utilizes the You Only Look Once version 7 (YOLOv7) tool to detect varying-sized craters in SAR images. It also employs the Markov random field (MRF) algorithm to identify surface rocks, which are usually difficult to detect in DEM data. The results are validated by optical images and DEM data in non-PSR. With the assistance of the DEM data, regions with slopes larger than 10° are excluded. YOLOv7 and MRF are applied to detect craters and rocky surfaces and exclude regions with steep slopes in the PSRs of craters Shoemaker, Slater, and Shackleton, respectively. This study proves SAR images are feasible in the selection of landing sites in the PSRs for future missions. |
format |
Article in Journal/Newspaper |
author |
Tong Xia Xuancheng Ren Yuntian Liu Niutao Liu Feng Xu Ya-Qiu Jin |
author_facet |
Tong Xia Xuancheng Ren Yuntian Liu Niutao Liu Feng Xu Ya-Qiu Jin |
author_sort |
Tong Xia |
title |
Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images |
title_short |
Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images |
title_full |
Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images |
title_fullStr |
Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images |
title_full_unstemmed |
Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images |
title_sort |
detection of surface rocks and small craters in permanently shadowed regions of the lunar south pole based on yolov7 and markov random field algorithms in sar images |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/rs16111834 https://doaj.org/article/6839eef0764f41c09b694d370feff5fc |
geographic |
Shackleton South Pole |
geographic_facet |
Shackleton South Pole |
genre |
South pole |
genre_facet |
South pole |
op_source |
Remote Sensing, Vol 16, Iss 11, p 1834 (2024) |
op_relation |
https://www.mdpi.com/2072-4292/16/11/1834 https://doaj.org/toc/2072-4292 doi:10.3390/rs16111834 2072-4292 https://doaj.org/article/6839eef0764f41c09b694d370feff5fc |
op_doi |
https://doi.org/10.3390/rs16111834 |
container_title |
Remote Sensing |
container_volume |
16 |
container_issue |
11 |
container_start_page |
1834 |
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1809943050903879680 |