End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliab...
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ftdoajarticles:oai:doaj.org/article:3e7032a2bc684da782db5e4552f11979 2023-05-15T13:58:38+02:00 End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery Yiheng Cai Shaobin Hu Shinan Lang Yajun Guo Jiaqi Liu 2020-04-01T00:00:00Z https://doi.org/10.3390/app10072501 https://doaj.org/article/3e7032a2bc684da782db5e4552f11979 EN eng MDPI AG https://www.mdpi.com/2076-3417/10/7/2501 https://doaj.org/toc/2076-3417 doi:10.3390/app10072501 2076-3417 https://doaj.org/article/3e7032a2bc684da782db5e4552f11979 Applied Sciences, Vol 10, Iss 2501, p 2501 (2020) end-to-end network classification ice sheet subsurface targets radar imagery Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 article 2020 ftdoajarticles https://doi.org/10.3390/app10072501 2022-12-31T12:52:21Z Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliable analysis of their future evolution. Newer deep learning techniques greatly outperform the traditional techniques based on hand-crafted feature engineering. Therefore, we propose an efficient end-to-end network for the automatic classification of ice sheet subsurface targets in radar imagery. Our network uses bilateral filtering to reduce noise and consists of ResNet module, improved Atrous Spatial Pyramid Pooling (ASPP) module, and decoder module. With radar images provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011 as our training and testing data, experimental results confirm the robustness and effectiveness of the proposed network in radargram. Article in Journal/Newspaper Antarc* Antarctica Greenland Ice Sheet Directory of Open Access Journals: DOAJ Articles Greenland Pyramid ENVELOPE(157.300,157.300,-81.333,-81.333) Applied Sciences 10 7 2501 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
end-to-end network classification ice sheet subsurface targets radar imagery Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
spellingShingle |
end-to-end network classification ice sheet subsurface targets radar imagery Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yiheng Cai Shaobin Hu Shinan Lang Yajun Guo Jiaqi Liu End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery |
topic_facet |
end-to-end network classification ice sheet subsurface targets radar imagery Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
description |
Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliable analysis of their future evolution. Newer deep learning techniques greatly outperform the traditional techniques based on hand-crafted feature engineering. Therefore, we propose an efficient end-to-end network for the automatic classification of ice sheet subsurface targets in radar imagery. Our network uses bilateral filtering to reduce noise and consists of ResNet module, improved Atrous Spatial Pyramid Pooling (ASPP) module, and decoder module. With radar images provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011 as our training and testing data, experimental results confirm the robustness and effectiveness of the proposed network in radargram. |
format |
Article in Journal/Newspaper |
author |
Yiheng Cai Shaobin Hu Shinan Lang Yajun Guo Jiaqi Liu |
author_facet |
Yiheng Cai Shaobin Hu Shinan Lang Yajun Guo Jiaqi Liu |
author_sort |
Yiheng Cai |
title |
End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery |
title_short |
End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery |
title_full |
End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery |
title_fullStr |
End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery |
title_full_unstemmed |
End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery |
title_sort |
end-to-end classification network for ice sheet subsurface targets in radar imagery |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/app10072501 https://doaj.org/article/3e7032a2bc684da782db5e4552f11979 |
long_lat |
ENVELOPE(157.300,157.300,-81.333,-81.333) |
geographic |
Greenland Pyramid |
geographic_facet |
Greenland Pyramid |
genre |
Antarc* Antarctica Greenland Ice Sheet |
genre_facet |
Antarc* Antarctica Greenland Ice Sheet |
op_source |
Applied Sciences, Vol 10, Iss 2501, p 2501 (2020) |
op_relation |
https://www.mdpi.com/2076-3417/10/7/2501 https://doaj.org/toc/2076-3417 doi:10.3390/app10072501 2076-3417 https://doaj.org/article/3e7032a2bc684da782db5e4552f11979 |
op_doi |
https://doi.org/10.3390/app10072501 |
container_title |
Applied Sciences |
container_volume |
10 |
container_issue |
7 |
container_start_page |
2501 |
_version_ |
1766266996807172096 |