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...

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Published in:IEEE Transactions on Geoscience and Remote Sensing
Main Authors: Singha, Suman, Johansson, Malin, Hughes, Nick, Hvidegaard, Sine, Skourup, Henriette
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2018
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
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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//
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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