Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data
The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including t...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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ftdoajarticles:oai:doaj.org/article:71842fd8b20d47a286bb3e7e24de3bc7 2023-05-15T14:52:00+02:00 Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data Lian He Xiyi He Fengming Hui Yufang Ye Tianyu Zhang Xiao Cheng 2022-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2022.3170732 https://doaj.org/article/71842fd8b20d47a286bb3e7e24de3bc7 EN eng IEEE https://ieeexplore.ieee.org/document/9764387/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2022.3170732 https://doaj.org/article/71842fd8b20d47a286bb3e7e24de3bc7 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 3904-3915 (2022) Arctic sea ice Gaofen-3 polarimetric decomposition polarimetric synthetic aperture radar random forest Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2022 ftdoajarticles https://doi.org/10.1109/JSTARS.2022.3170732 2022-12-30T21:47:54Z The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14–18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°–46°) were superior to low incidence angles (21°–27°) due to the overall higher accuracies. Article in Journal/Newspaper Arctic Fram Strait Sea ice Directory of Open Access Journals: DOAJ Articles Arctic IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 3904 3915 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic sea ice Gaofen-3 polarimetric decomposition polarimetric synthetic aperture radar random forest Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Arctic sea ice Gaofen-3 polarimetric decomposition polarimetric synthetic aperture radar random forest Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Lian He Xiyi He Fengming Hui Yufang Ye Tianyu Zhang Xiao Cheng Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data |
topic_facet |
Arctic sea ice Gaofen-3 polarimetric decomposition polarimetric synthetic aperture radar random forest Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
description |
The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14–18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°–46°) were superior to low incidence angles (21°–27°) due to the overall higher accuracies. |
format |
Article in Journal/Newspaper |
author |
Lian He Xiyi He Fengming Hui Yufang Ye Tianyu Zhang Xiao Cheng |
author_facet |
Lian He Xiyi He Fengming Hui Yufang Ye Tianyu Zhang Xiao Cheng |
author_sort |
Lian He |
title |
Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data |
title_short |
Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data |
title_full |
Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data |
title_fullStr |
Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data |
title_full_unstemmed |
Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data |
title_sort |
investigation of polarimetric decomposition for arctic summer sea ice classification using gaofen-3 fully polarimetric sar data |
publisher |
IEEE |
publishDate |
2022 |
url |
https://doi.org/10.1109/JSTARS.2022.3170732 https://doaj.org/article/71842fd8b20d47a286bb3e7e24de3bc7 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Fram Strait Sea ice |
genre_facet |
Arctic Fram Strait Sea ice |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 3904-3915 (2022) |
op_relation |
https://ieeexplore.ieee.org/document/9764387/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2022.3170732 https://doaj.org/article/71842fd8b20d47a286bb3e7e24de3bc7 |
op_doi |
https://doi.org/10.1109/JSTARS.2022.3170732 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
15 |
container_start_page |
3904 |
op_container_end_page |
3915 |
_version_ |
1766323139508174848 |