Spacecraft Recovery & Remote Sensing, Volume. 45, Issue 1, 41(2024)
An Improved Deep Learning Method for Mapping Glacial Lakes Using Satellite Observation and Its Application
Figures & Tables(9)
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Table 1. Landsat-8 remote sensing images used in this study
View tableView in ArticleTable 1. Landsat-8 remote sensing images used in this study
轨道号 获取日期 云量/% 用途 轨道号 获取日期 云量/% 用途 P136R039 2013-09-28 3.49 模型训练 P135R039 2014-05-19 36.09 冰湖制图 P136R039 2021-09-18 7.57 模型训练 P135R039 2014-11-27 11.23 冰湖制图 P136R040 2018-10-28 18.92 模型训练 P135R039 2014-12-29 3.31 冰湖制图 P137R040 2020-10-24 1.79 模型训练 P135R039 2015-04-20 2.49 冰湖制图 P137R041 2016-10-29 2.92 模型训练 P135R039 2015-07-25 5.59 冰湖制图 P138R040 2021-12-21 0.79 模型训练 P135R039 2015-11-30 3.17 冰湖制图 P138R041 2018-11-27 2.11 模型训练 P135R039 2016-01-17 2.89 冰湖制图 P139R041 2016-12-30 2.17 模型训练 P135R039 2016-07-27 27.13 冰湖制图 P140R041 2020-10-29 3.62 模型训练 P135R039 2016-10-31 15.20 冰湖制图 P141R040 2018-09-29 7.79 模型训练 P135R039 2017-07-14 42.81 冰湖制图 P142R036 2015-09-12 0.21 模型训练 P135R039 2017-07-30 57.64 冰湖制图 P142R040 2020-10-11 2.40 模型训练 P135R039 2017-10-18 24.11 冰湖制图 P143R039 2018-09-27 3.37 模型训练 P135R039 2017-12-21 3.47 冰湖制图 P143R040 2020-10-18 4.32 模型训练 P135R039 2018-01-22 1.81 冰湖制图 P144R039 2020-10-09 1.53 模型训练 P135R039 2018-09-19 37.01 冰湖制图 P145R039 2020-10-16 1.55 模型训练 P135R039 2018-11-22 1.02 冰湖制图 P146R038 2021-10-10 1.66 模型训练 P135R039 2019-06-02 12.05 冰湖制图 P147R036 2019-09-10 1.61 模型训练 P135R039 2019-07-20 73.94 冰湖制图 P147R037 2019-09-10 1.00 模型训练 P135R039 2019-09-06 70.89 冰湖制图 P148R036 2018-08-29 5.56 模型训练 P135R039 2020-10-10 35.64 冰湖制图 P149R031 2016-09-15 0.65 模型训练 P135R039 2020-11-11 2.90 冰湖制图 P149R036 2019-09-24 8.73 模型训练 P135R039 2021-07-25 26.95 冰湖制图 P151R034 2016-09-29 1.04 模型训练 P135R039 2021-10-29 10.15 冰湖制图 P151R034 2021-09-27 0.36 模型训练 P135R039 2022-05-01 17.91 冰湖制图 P135R039 2013-08-04 19.73 冰湖制图 P135R039 2022-05-25 41.40 冰湖制图 P135R039 2013-08-20 38.64 冰湖制图 P135R039 2022-07-04 29.23 冰湖制图 P135R039 2013-11-24 2.07 冰湖制图
Table 2. Comparison of the mapping accuracy of the training models
View tableView in ArticleTable 2. Comparison of the mapping accuracy of the training models
模型 P R I F1 PSPNet 0.902 7 0.875 6 0.800 1 0.888 9 DeepLabV3+ 0.921 4 0.966 3 0.892 7 0.943 3 原生的U-Net 0.926 1 0.911 3 0.849 6 0.918 6 改进的U-Net 0.972 5 0.966 5 0.940 8 0.969 4
Table 3. Statistics of glacial Lakes mapped by the improved U-Net model
View tableView in ArticleTable 3. Statistics of glacial Lakes mapped by the improved U-Net model
年份 数量 总面积/km2 重叠率/% 生产者精度/% 用户精度/% 总体精度/% Kappa系数 云雪等导致 错提冰湖数量 云雪等导致 错提冰湖比例/% 2013 353 42.35 75.91 75.56 99.27 88.16 0.76 46 23.47 2014 239 33.70 60.97 52.78 98.96 77.37 0.54 87 46.28 2015 405 42.21 75.98 90.56 98.79 95.00 0.90 17 9.04 2016 408 43.02 75.24 94.44 99.42 97.11 0.94 10 5.32 2017 301 42.60 77.75 67.78 98.39 84.21 0.68 58 30.85 2018 216 36.41 67.42 56.11 97.12 78.42 0.56 79 42.02 2019 199 37.16 67.11 54.44 97.03 77.63 0.54 82 43.62 2020 404 50.01 96.66 96.67 99.43 98.16 0.96 6 3.19 2021 403 48.38 84.73 77.78 98.59 88.95 0.78 40 21.28 2022 382 48.23 86.34 88.89 98.77 94.21 0.88 23 10.53
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Ningtao YANG, Yong NIE. An Improved Deep Learning Method for Mapping Glacial Lakes Using Satellite Observation and Its Application[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(1): 41
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Received: Aug. 22, 2023
Accepted: Nov. 6, 2023
Published Online: Apr. 22, 2024
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