Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements
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, a...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
IEEE - Institute of Electrical and Electronics Engineers
2018
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Subjects: | |
Online Access: | https://elib.dlr.de/113943/ https://elib.dlr.de/113943/1/Singha_IEEE_TGRS_2018_small.pdf https://doi.org/10.1109/TGRS.2018.2809504 |
_version_ | 1835010208060932096 |
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author | Singha, Suman Johansson, A. Malin Hughes, Nicholas Hvidegaard, Sine M. Skourup, Henriette |
author_facet | Singha, Suman Johansson, A. Malin Hughes, Nicholas Hvidegaard, Sine M. Skourup, Henriette |
author_sort | Singha, Suman |
collection | Unknown |
container_issue | 7 |
container_start_page | 3715 |
container_title | IEEE Transactions on Geoscience and Remote Sensing |
container_volume | 56 |
description | 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 | ftdlr:oai:elib.dlr.de:113943 |
institution | Open Polar |
language | English |
op_collection_id | ftdlr |
op_container_end_page | 3734 |
op_doi | https://doi.org/10.1109/TGRS.2018.2809504 |
op_relation | https://elib.dlr.de/113943/1/Singha_IEEE_TGRS_2018_small.pdf Singha, Suman und Johansson, A. Malin und Hughes, Nicholas und Hvidegaard, Sine M. und Skourup, Henriette (2018) Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements. IEEE Transactions on Geoscience and Remote Sensing, 56 (7), Seiten 3715-3734. IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/TGRS.2018.2809504 <https://doi.org/10.1109/TGRS.2018.2809504>. ISSN 0196-2892. |
op_rights | info:eu-repo/semantics/openAccess |
publishDate | 2018 |
publisher | IEEE - Institute of Electrical and Electronics Engineers |
record_format | openpolar |
spelling | ftdlr:oai:elib.dlr.de:113943 2025-06-15T14:17:35+00:00 Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements Singha, Suman Johansson, A. Malin Hughes, Nicholas Hvidegaard, Sine M. Skourup, Henriette 2018-04-26 application/pdf https://elib.dlr.de/113943/ https://elib.dlr.de/113943/1/Singha_IEEE_TGRS_2018_small.pdf https://doi.org/10.1109/TGRS.2018.2809504 en eng IEEE - Institute of Electrical and Electronics Engineers https://elib.dlr.de/113943/1/Singha_IEEE_TGRS_2018_small.pdf Singha, Suman und Johansson, A. Malin und Hughes, Nicholas und Hvidegaard, Sine M. und Skourup, Henriette (2018) Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements. IEEE Transactions on Geoscience and Remote Sensing, 56 (7), Seiten 3715-3734. IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/TGRS.2018.2809504 <https://doi.org/10.1109/TGRS.2018.2809504>. ISSN 0196-2892. info:eu-repo/semantics/openAccess SAR-Signalverarbeitung Zeitschriftenbeitrag PeerReviewed info:eu-repo/semantics/article 2018 ftdlr https://doi.org/10.1109/TGRS.2018.2809504 2025-06-04T04:58:09Z 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 Unknown Arctic IEEE Transactions on Geoscience and Remote Sensing 56 7 3715 3734 |
spellingShingle | SAR-Signalverarbeitung Singha, Suman Johansson, A. Malin Hughes, Nicholas Hvidegaard, Sine M. 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 | SAR-Signalverarbeitung |
topic_facet | SAR-Signalverarbeitung |
url | https://elib.dlr.de/113943/ https://elib.dlr.de/113943/1/Singha_IEEE_TGRS_2018_small.pdf https://doi.org/10.1109/TGRS.2018.2809504 |