Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs

Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	XIAO Xiangwen [verfasserIn] SHEN Xiaoyi [verfasserIn] KE Changqing [verfasserIn] ZHOU Xinghua [verfasserIn]

Format:	E-Artikel
Sprache:	Chinesisch

Erschienen:	2020

Schlagwörter:	iceberg machine learning sentinel-1a sar

Übergeordnetes Werk:	In: Acta Geodaetica et Cartographica Sinica - Surveying and Mapping Press, 2014, 49(2020), 4, Seite 509-521
Übergeordnetes Werk:	volume:49 ; year:2020 ; number:4 ; pages:509-521

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.11947/j.AGCS.2020.20190174

Katalog-ID:	DOAJ074256203

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allfields	10.11947/j.AGCS.2020.20190174 doi (DE-627)DOAJ074256203 (DE-599)DOAJ3641b8957a014819a6859fb07856b9e3 DE-627 ger DE-627 rakwb chi GA1-1776 XIAO Xiangwen verfasserin aut Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection. iceberg machine learning sentinel-1a sar Mathematical geography. Cartography SHEN Xiaoyi verfasserin aut KE Changqing verfasserin aut ZHOU Xinghua verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 49(2020), 4, Seite 509-521 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:49 year:2020 number:4 pages:509-521 https://doi.org/10.11947/j.AGCS.2020.20190174 kostenfrei https://doaj.org/article/3641b8957a014819a6859fb07856b9e3 kostenfrei http://html.rhhz.net/CHXB/html/2020-4-509.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 49 2020 4 509-521
spelling	10.11947/j.AGCS.2020.20190174 doi (DE-627)DOAJ074256203 (DE-599)DOAJ3641b8957a014819a6859fb07856b9e3 DE-627 ger DE-627 rakwb chi GA1-1776 XIAO Xiangwen verfasserin aut Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection. iceberg machine learning sentinel-1a sar Mathematical geography. Cartography SHEN Xiaoyi verfasserin aut KE Changqing verfasserin aut ZHOU Xinghua verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 49(2020), 4, Seite 509-521 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:49 year:2020 number:4 pages:509-521 https://doi.org/10.11947/j.AGCS.2020.20190174 kostenfrei https://doaj.org/article/3641b8957a014819a6859fb07856b9e3 kostenfrei http://html.rhhz.net/CHXB/html/2020-4-509.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 49 2020 4 509-521
allfields_unstemmed	10.11947/j.AGCS.2020.20190174 doi (DE-627)DOAJ074256203 (DE-599)DOAJ3641b8957a014819a6859fb07856b9e3 DE-627 ger DE-627 rakwb chi GA1-1776 XIAO Xiangwen verfasserin aut Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection. iceberg machine learning sentinel-1a sar Mathematical geography. Cartography SHEN Xiaoyi verfasserin aut KE Changqing verfasserin aut ZHOU Xinghua verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 49(2020), 4, Seite 509-521 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:49 year:2020 number:4 pages:509-521 https://doi.org/10.11947/j.AGCS.2020.20190174 kostenfrei https://doaj.org/article/3641b8957a014819a6859fb07856b9e3 kostenfrei http://html.rhhz.net/CHXB/html/2020-4-509.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 49 2020 4 509-521
allfieldsGer	10.11947/j.AGCS.2020.20190174 doi (DE-627)DOAJ074256203 (DE-599)DOAJ3641b8957a014819a6859fb07856b9e3 DE-627 ger DE-627 rakwb chi GA1-1776 XIAO Xiangwen verfasserin aut Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection. iceberg machine learning sentinel-1a sar Mathematical geography. Cartography SHEN Xiaoyi verfasserin aut KE Changqing verfasserin aut ZHOU Xinghua verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 49(2020), 4, Seite 509-521 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:49 year:2020 number:4 pages:509-521 https://doi.org/10.11947/j.AGCS.2020.20190174 kostenfrei https://doaj.org/article/3641b8957a014819a6859fb07856b9e3 kostenfrei http://html.rhhz.net/CHXB/html/2020-4-509.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 49 2020 4 509-521
allfieldsSound	10.11947/j.AGCS.2020.20190174 doi (DE-627)DOAJ074256203 (DE-599)DOAJ3641b8957a014819a6859fb07856b9e3 DE-627 ger DE-627 rakwb chi GA1-1776 XIAO Xiangwen verfasserin aut Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection. iceberg machine learning sentinel-1a sar Mathematical geography. Cartography SHEN Xiaoyi verfasserin aut KE Changqing verfasserin aut ZHOU Xinghua verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 49(2020), 4, Seite 509-521 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:49 year:2020 number:4 pages:509-521 https://doi.org/10.11947/j.AGCS.2020.20190174 kostenfrei https://doaj.org/article/3641b8957a014819a6859fb07856b9e3 kostenfrei http://html.rhhz.net/CHXB/html/2020-4-509.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 49 2020 4 509-521
language	Chinese
source	In Acta Geodaetica et Cartographica Sinica 49(2020), 4, Seite 509-521 volume:49 year:2020 number:4 pages:509-521
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institution	findex.gbv.de
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topic_title	GA1-1776 Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs iceberg machine learning sentinel-1a sar
topic	misc GA1-1776 misc iceberg misc machine learning misc sentinel-1a misc sar misc Mathematical geography. Cartography
topic_unstemmed	misc GA1-1776 misc iceberg misc machine learning misc sentinel-1a misc sar misc Mathematical geography. Cartography
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title	Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs
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title_full	Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs
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journal	Acta Geodaetica et Cartographica Sinica
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author_browse	XIAO Xiangwen SHEN Xiaoyi KE Changqing ZHOU Xinghua
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title_sort	comparison of machine learning algorithms based on sentinel-1a data to detect icebergs
callnumber	GA1-1776
title_auth	Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs
abstract	Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection.
abstractGer	Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection.
abstract_unstemmed	Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection.
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title_short	Comparison of machine learning algorithms based on Sentinel-1A data to detect icebergs
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