Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements
Accepted manuscript version. Published version available at https://doi.org/10.1109/TGRS.2018.2809504 . In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
IEEE
2018
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Subjects: | |
Online Access: | https://hdl.handle.net/10037/14562 https://doi.org/10.1109/TGRS.2018.2809504 |
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author | Singha, Suman Johansson, Malin Hughes, Nick Hvidegaard, Sine Skourup, Henriette |
author_facet | Singha, Suman Johansson, Malin Hughes, Nick Hvidegaard, Sine Skourup, Henriette |
author_sort | Singha, Suman |
collection | University of Tromsø: Munin Open Research Archive |
container_issue | 7 |
container_start_page | 3715 |
container_title | IEEE Transactions on Geoscience and Remote Sensing |
container_volume | 56 |
description | Accepted manuscript version. Published version available at https://doi.org/10.1109/TGRS.2018.2809504 . In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements. |
format | Article in Journal/Newspaper |
genre | Arctic Arctic Sea ice |
genre_facet | Arctic Arctic Sea ice |
geographic | Arctic |
geographic_facet | Arctic |
id | ftunivtroemsoe:oai:munin.uit.no:10037/14562 |
institution | Open Polar |
language | English |
op_collection_id | ftunivtroemsoe |
op_container_end_page | 3734 |
op_doi | https://doi.org/10.1109/TGRS.2018.2809504 |
op_relation | IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/EC/ENV.2013.6.1-1/603887/EU/Ice, Climate, and Economics - Arctic Research on Change/ICE-ARC/ info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ info:eu-repo/grantAgreement/RCN/NORRUSS/233896/Norway/Detection and Characterization of Anthropogenic Oil Pollution in the Barents Sea by Synthetic Aperture Radar// FRIDAID 1567926 doi:10.1109/TGRS.2018.2809504 https://hdl.handle.net/10037/14562 |
op_rights | openAccess |
publishDate | 2018 |
publisher | IEEE |
record_format | openpolar |
spelling | ftunivtroemsoe:oai:munin.uit.no:10037/14562 2025-04-13T14:11:34+00:00 Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements Singha, Suman Johansson, Malin Hughes, Nick Hvidegaard, Sine Skourup, Henriette 2018-04-26 https://hdl.handle.net/10037/14562 https://doi.org/10.1109/TGRS.2018.2809504 eng eng IEEE IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/EC/ENV.2013.6.1-1/603887/EU/Ice, Climate, and Economics - Arctic Research on Change/ICE-ARC/ info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ info:eu-repo/grantAgreement/RCN/NORRUSS/233896/Norway/Detection and Characterization of Anthropogenic Oil Pollution in the Barents Sea by Synthetic Aperture Radar// FRIDAID 1567926 doi:10.1109/TGRS.2018.2809504 https://hdl.handle.net/10037/14562 openAccess VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 Journal article Tidsskriftartikkel Peer reviewed 2018 ftunivtroemsoe https://doi.org/10.1109/TGRS.2018.2809504 2025-03-14T05:17:56Z Accepted manuscript version. Published version available at https://doi.org/10.1109/TGRS.2018.2809504 . In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements. Article in Journal/Newspaper Arctic Arctic Sea ice University of Tromsø: Munin Open Research Archive Arctic IEEE Transactions on Geoscience and Remote Sensing 56 7 3715 3734 |
spellingShingle | VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 Singha, Suman Johansson, Malin Hughes, Nick Hvidegaard, Sine Skourup, Henriette Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements |
title | Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements |
title_full | Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements |
title_fullStr | Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements |
title_full_unstemmed | Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements |
title_short | Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements |
title_sort | arctic sea ice characterization using spaceborne fully polarimetric l-, c- and x-band sar with validation by airborne measurements |
topic | VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 |
topic_facet | VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 |
url | https://hdl.handle.net/10037/14562 https://doi.org/10.1109/TGRS.2018.2809504 |