A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection

Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately ext...
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Autor*in:	Xiaofei Guo [verfasserIn] Jianhua Wan [verfasserIn] Shanwei Liu [verfasserIn] Mingming Xu [verfasserIn] Hui Sheng [verfasserIn] Muhammad Yasir [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	sea fog MODIS CALIOP scSE-LinkNet

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 24, p 5163
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:24, p 5163

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13245163

Katalog-ID:	DOAJ019244665

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520			\|a Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.
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allfields	10.3390/rs13245163 doi (DE-627)DOAJ019244665 (DE-599)DOAJe19caa51a0da418082ad5f11a1cef616 DE-627 ger DE-627 rakwb eng Xiaofei Guo verfasserin aut A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products. sea fog MODIS CALIOP scSE-LinkNet Science Q Jianhua Wan verfasserin aut Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Hui Sheng verfasserin aut Muhammad Yasir verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 24, p 5163 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:24, p 5163 https://doi.org/10.3390/rs13245163 kostenfrei https://doaj.org/article/e19caa51a0da418082ad5f11a1cef616 kostenfrei https://www.mdpi.com/2072-4292/13/24/5163 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 13 2021 24, p 5163
spelling	10.3390/rs13245163 doi (DE-627)DOAJ019244665 (DE-599)DOAJe19caa51a0da418082ad5f11a1cef616 DE-627 ger DE-627 rakwb eng Xiaofei Guo verfasserin aut A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products. sea fog MODIS CALIOP scSE-LinkNet Science Q Jianhua Wan verfasserin aut Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Hui Sheng verfasserin aut Muhammad Yasir verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 24, p 5163 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:24, p 5163 https://doi.org/10.3390/rs13245163 kostenfrei https://doaj.org/article/e19caa51a0da418082ad5f11a1cef616 kostenfrei https://www.mdpi.com/2072-4292/13/24/5163 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 13 2021 24, p 5163
allfields_unstemmed	10.3390/rs13245163 doi (DE-627)DOAJ019244665 (DE-599)DOAJe19caa51a0da418082ad5f11a1cef616 DE-627 ger DE-627 rakwb eng Xiaofei Guo verfasserin aut A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products. sea fog MODIS CALIOP scSE-LinkNet Science Q Jianhua Wan verfasserin aut Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Hui Sheng verfasserin aut Muhammad Yasir verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 24, p 5163 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:24, p 5163 https://doi.org/10.3390/rs13245163 kostenfrei https://doaj.org/article/e19caa51a0da418082ad5f11a1cef616 kostenfrei https://www.mdpi.com/2072-4292/13/24/5163 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 13 2021 24, p 5163
allfieldsGer	10.3390/rs13245163 doi (DE-627)DOAJ019244665 (DE-599)DOAJe19caa51a0da418082ad5f11a1cef616 DE-627 ger DE-627 rakwb eng Xiaofei Guo verfasserin aut A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products. sea fog MODIS CALIOP scSE-LinkNet Science Q Jianhua Wan verfasserin aut Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Hui Sheng verfasserin aut Muhammad Yasir verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 24, p 5163 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:24, p 5163 https://doi.org/10.3390/rs13245163 kostenfrei https://doaj.org/article/e19caa51a0da418082ad5f11a1cef616 kostenfrei https://www.mdpi.com/2072-4292/13/24/5163 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 13 2021 24, p 5163
allfieldsSound	10.3390/rs13245163 doi (DE-627)DOAJ019244665 (DE-599)DOAJe19caa51a0da418082ad5f11a1cef616 DE-627 ger DE-627 rakwb eng Xiaofei Guo verfasserin aut A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products. sea fog MODIS CALIOP scSE-LinkNet Science Q Jianhua Wan verfasserin aut Shanwei Liu verfasserin aut Mingming Xu verfasserin aut Hui Sheng verfasserin aut Muhammad Yasir verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 24, p 5163 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:24, p 5163 https://doi.org/10.3390/rs13245163 kostenfrei https://doaj.org/article/e19caa51a0da418082ad5f11a1cef616 kostenfrei https://www.mdpi.com/2072-4292/13/24/5163 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 13 2021 24, p 5163
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author	Xiaofei Guo
spellingShingle	Xiaofei Guo misc sea fog misc MODIS misc CALIOP misc scSE-LinkNet misc Science misc Q A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection
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topic_title	A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection sea fog MODIS CALIOP scSE-LinkNet
topic	misc sea fog misc MODIS misc CALIOP misc scSE-LinkNet misc Science misc Q
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title	A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection
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title_full	A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection
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title_sort	scse-linknet deep learning model for daytime sea fog detection
title_auth	A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection
abstract	Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.
abstractGer	Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.
abstract_unstemmed	Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (<i<POD</i<), false alarm rate (<i<FAR</i<), critical success index (<i<CSI</i<), and Heidke skill scores (<i<HSS</i<) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the <i<CSI</i< of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.
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title_short	A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection
url	https://doi.org/10.3390/rs13245163 https://doaj.org/article/e19caa51a0da418082ad5f11a1cef616 https://www.mdpi.com/2072-4292/13/24/5163 https://doaj.org/toc/2072-4292
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author2	Jianhua Wan Shanwei Liu Mingming Xu Hui Sheng Muhammad Yasir
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up_date	2024-07-03T22:34:40.550Z
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A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection

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