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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ftdtupubl:oai:pure.atira.dk:publications/90a08a69-d82b-43ea-8ba4-d2afabfd3299 2024-06-23T07:48:35+00:00 Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements Singha, Suman Johansson, Malin Hughes, Nicholas Hvidegaard, Sine Munk Skourup, Henriette 2018 application/pdf https://orbit.dtu.dk/en/publications/90a08a69-d82b-43ea-8ba4-d2afabfd3299 https://doi.org/10.1109/TGRS.2018.2809504 https://backend.orbit.dtu.dk/ws/files/150063193/08350411.pdf eng eng https://orbit.dtu.dk/en/publications/90a08a69-d82b-43ea-8ba4-d2afabfd3299 info:eu-repo/semantics/openAccess Singha , S , Johansson , M , Hughes , N , Hvidegaard , S M & Skourup , H 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 , vol. 56 , no. 7 , pp. 3715-3734 . https://doi.org/10.1109/TGRS.2018.2809504 article 2018 ftdtupubl https://doi.org/10.1109/TGRS.2018.2809504 2024-06-04T15:19:45Z 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 Technical University of Denmark: DTU Orbit Arctic IEEE Transactions on Geoscience and Remote Sensing 56 7 3715 3734 |
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Open Polar |
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Technical University of Denmark: DTU Orbit |
op_collection_id |
ftdtupubl |
language |
English |
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 |
author |
Singha, Suman Johansson, Malin Hughes, Nicholas Hvidegaard, Sine Munk Skourup, Henriette |
spellingShingle |
Singha, Suman Johansson, Malin Hughes, Nicholas Hvidegaard, Sine Munk Skourup, Henriette Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements |
author_facet |
Singha, Suman Johansson, Malin Hughes, Nicholas Hvidegaard, Sine Munk Skourup, Henriette |
author_sort |
Singha, Suman |
title |
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_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_sort |
arctic sea ice characterization using spaceborne fully polarimetric l-, c-, and x-band sar with validation by airborne measurements |
publishDate |
2018 |
url |
https://orbit.dtu.dk/en/publications/90a08a69-d82b-43ea-8ba4-d2afabfd3299 https://doi.org/10.1109/TGRS.2018.2809504 https://backend.orbit.dtu.dk/ws/files/150063193/08350411.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_source |
Singha , S , Johansson , M , Hughes , N , Hvidegaard , S M & Skourup , H 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 , vol. 56 , no. 7 , pp. 3715-3734 . https://doi.org/10.1109/TGRS.2018.2809504 |
op_relation |
https://orbit.dtu.dk/en/publications/90a08a69-d82b-43ea-8ba4-d2afabfd3299 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1109/TGRS.2018.2809504 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
56 |
container_issue |
7 |
container_start_page |
3715 |
op_container_end_page |
3734 |
_version_ |
1802638951497859072 |