date: 2024-07-19T07:37:29Z
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pdf:docinfo:title: Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding
xmp:CreatorTool: LaTeX with hyperref
Keywords: delay-Doppler maps (DDMs); Global Navigation Satellite System-Reflectometry (GNSS-R); local linear embedding (LLE); sea ice detection
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subject: Sea ice plays a critical role in the Earth?s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70 north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed.
dc:creator: Yuan Hu, Xifan Hua, Qingyun Yan, Wei Liu, Zhihao Jiang and Jens Wickert
dcterms:created: 2024-07-19T07:34:48Z
Last-Modified: 2024-07-19T07:37:29Z
dcterms:modified: 2024-07-19T07:37:29Z
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title: Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding
Last-Save-Date: 2024-07-19T07:37:29Z
pdf:docinfo:creator_tool: LaTeX with hyperref
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pdf:docinfo:keywords: delay-Doppler maps (DDMs); Global Navigation Satellite System-Reflectometry (GNSS-R); local linear embedding (LLE); sea ice detection
pdf:docinfo:modified: 2024-07-19T07:37:29Z
meta:save-date: 2024-07-19T07:37:29Z
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dc:title: Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding
modified: 2024-07-19T07:37:29Z
cp:subject: Sea ice plays a critical role in the Earth?s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70 north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed.
pdf:docinfo:subject: Sea ice plays a critical role in the Earth?s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70 north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed.
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pdf:docinfo:creator: Yuan Hu, Xifan Hua, Qingyun Yan, Wei Liu, Zhihao Jiang and Jens Wickert
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creator: Yuan Hu, Xifan Hua, Qingyun Yan, Wei Liu, Zhihao Jiang and Jens Wickert
meta:author: Yuan Hu, Xifan Hua, Qingyun Yan, Wei Liu, Zhihao Jiang and Jens Wickert
dc:subject: delay-Doppler maps (DDMs); Global Navigation Satellite System-Reflectometry (GNSS-R); local linear embedding (LLE); sea ice detection
meta:creation-date: 2024-07-19T07:34:48Z
created: Fri Jul 19 09:34:48 CEST 2024
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Creation-Date: 2024-07-19T07:34:48Z
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meta:keyword: delay-Doppler maps (DDMs); Global Navigation Satellite System-Reflectometry (GNSS-R); local linear embedding (LLE); sea ice detection
Author: Yuan Hu, Xifan Hua, Qingyun Yan, Wei Liu, Zhihao Jiang and Jens Wickert
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