Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods
Remote sensing extraction of glacial lakes is an effective way of monitoring water body distribution and outburst events. At present, the lack of glacial lake datasets and the edge recognition problem of semantic segmentation networks lead to poor accuracy and inaccurate outlines of glacial lakes. T...
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Copernicus Publications
2024
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00070918 2024-02-11T10:09:08+01:00 Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods Cao, Yungang Pan, Rumeng Pan, Meng Lei, Ruodan Du, Puying Bai, Xueqin 2024-01 electronic https://doi.org/10.5194/tc-18-153-2024 https://noa.gwlb.de/receive/cop_mods_00070918 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00069243/tc-18-153-2024.pdf https://tc.copernicus.org/articles/18/153/2024/tc-18-153-2024.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-18-153-2024 https://noa.gwlb.de/receive/cop_mods_00070918 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00069243/tc-18-153-2024.pdf https://tc.copernicus.org/articles/18/153/2024/tc-18-153-2024.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2024 ftnonlinearchiv https://doi.org/10.5194/tc-18-153-2024 2024-01-15T00:22:45Z Remote sensing extraction of glacial lakes is an effective way of monitoring water body distribution and outburst events. At present, the lack of glacial lake datasets and the edge recognition problem of semantic segmentation networks lead to poor accuracy and inaccurate outlines of glacial lakes. Therefore, this study constructed a high-resolution dataset containing seven types of glacial lakes and proposed a refined glacial lake extraction method, which combines the LinkNet50 network for rough extraction and simple linear iterative clustering (SLIC) dense conditional random field (DenseCRF) for optimization. The results show that (1) with Google Earth images of 0.52 m resolution in the study area, the recall, precision, F1 score, and intersection over union (IoU) of glacial lake extraction based on the proposed method are 96.52 %, 92.49 %, 94.46 %, and 90.69 %, respectively, and (2) with the Google Earth images of 2.11 m resolution in the Qomolangma National Nature Reserve, 2300 glacial lakes with a total area of 65.17 km2 were detected by the proposed method. The area of the minimum glacial lake that can be extracted is 160 m2 (less than 6×6 pixels). This method has advantages in small glacial lake extraction and refined outline detection, which can be applied to extracting glacial lakes in the high-Asia region with high-resolution images. Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA Glacial Lake ENVELOPE(-129.463,-129.463,58.259,58.259) The Cryosphere 18 1 153 168 |
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Niedersächsisches Online-Archiv NOA |
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ftnonlinearchiv |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Cao, Yungang Pan, Rumeng Pan, Meng Lei, Ruodan Du, Puying Bai, Xueqin Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods |
topic_facet |
article Verlagsveröffentlichung |
description |
Remote sensing extraction of glacial lakes is an effective way of monitoring water body distribution and outburst events. At present, the lack of glacial lake datasets and the edge recognition problem of semantic segmentation networks lead to poor accuracy and inaccurate outlines of glacial lakes. Therefore, this study constructed a high-resolution dataset containing seven types of glacial lakes and proposed a refined glacial lake extraction method, which combines the LinkNet50 network for rough extraction and simple linear iterative clustering (SLIC) dense conditional random field (DenseCRF) for optimization. The results show that (1) with Google Earth images of 0.52 m resolution in the study area, the recall, precision, F1 score, and intersection over union (IoU) of glacial lake extraction based on the proposed method are 96.52 %, 92.49 %, 94.46 %, and 90.69 %, respectively, and (2) with the Google Earth images of 2.11 m resolution in the Qomolangma National Nature Reserve, 2300 glacial lakes with a total area of 65.17 km2 were detected by the proposed method. The area of the minimum glacial lake that can be extracted is 160 m2 (less than 6×6 pixels). This method has advantages in small glacial lake extraction and refined outline detection, which can be applied to extracting glacial lakes in the high-Asia region with high-resolution images. |
format |
Article in Journal/Newspaper |
author |
Cao, Yungang Pan, Rumeng Pan, Meng Lei, Ruodan Du, Puying Bai, Xueqin |
author_facet |
Cao, Yungang Pan, Rumeng Pan, Meng Lei, Ruodan Du, Puying Bai, Xueqin |
author_sort |
Cao, Yungang |
title |
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods |
title_short |
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods |
title_full |
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods |
title_fullStr |
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods |
title_full_unstemmed |
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods |
title_sort |
refined glacial lake extraction in a high-asia region by deep neural network and superpixel-based conditional random field methods |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/tc-18-153-2024 https://noa.gwlb.de/receive/cop_mods_00070918 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00069243/tc-18-153-2024.pdf https://tc.copernicus.org/articles/18/153/2024/tc-18-153-2024.pdf |
long_lat |
ENVELOPE(-129.463,-129.463,58.259,58.259) |
geographic |
Glacial Lake |
geographic_facet |
Glacial Lake |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-18-153-2024 https://noa.gwlb.de/receive/cop_mods_00070918 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00069243/tc-18-153-2024.pdf https://tc.copernicus.org/articles/18/153/2024/tc-18-153-2024.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-18-153-2024 |
container_title |
The Cryosphere |
container_volume |
18 |
container_issue |
1 |
container_start_page |
153 |
op_container_end_page |
168 |
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1790608865831682048 |