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...

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Published in:IEEE Transactions on Geoscience and Remote Sensing
Main Authors: Singha, Suman, Johansson, Malin, Hughes, Nicholas, Hvidegaard, Sine Munk, Skourup, Henriette
Format: Article in Journal/Newspaper
Language:English
Published: 2018
Subjects:
Online Access: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
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spelling 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
institution Open Polar
collection 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
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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
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