Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification
This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Propos...
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Multidisciplinary Digital Publishing Institute (MDPI)
2016
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Online Access: | https://elib.dlr.de/98218/ https://elib.dlr.de/98218/1/remotesensing-08-00198-v3.pdf http://www.mdpi.com/2072-4292/8/3/198 |
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ftdlr:oai:elib.dlr.de:98218 2023-12-03T10:15:00+01:00 Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification Ressel, Rudolf Singha, Suman 2016-02-29 application/pdf https://elib.dlr.de/98218/ https://elib.dlr.de/98218/1/remotesensing-08-00198-v3.pdf http://www.mdpi.com/2072-4292/8/3/198 de ger Multidisciplinary Digital Publishing Institute (MDPI) https://elib.dlr.de/98218/1/remotesensing-08-00198-v3.pdf Ressel, Rudolf und Singha, Suman (2016) Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification. Remote Sensing, 8 (3), Seiten 1-27. Multidisciplinary Digital Publishing Institute (MDPI). doi:10.3390/rs8030198 <https://doi.org/10.3390/rs8030198>. ISSN 2072-4292. Institut für Methodik der Fernerkundung SAR-Signalverarbeitung Zeitschriftenbeitrag PeerReviewed 2016 ftdlr https://doi.org/10.3390/rs8030198 2023-11-06T00:23:49Z This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which 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, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types Article in Journal/Newspaper Arctic Arctic Sea ice German Aerospace Center: elib - DLR electronic library Arctic Remote Sensing 8 3 198 |
institution |
Open Polar |
collection |
German Aerospace Center: elib - DLR electronic library |
op_collection_id |
ftdlr |
language |
German |
topic |
Institut für Methodik der Fernerkundung SAR-Signalverarbeitung |
spellingShingle |
Institut für Methodik der Fernerkundung SAR-Signalverarbeitung Ressel, Rudolf Singha, Suman Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification |
topic_facet |
Institut für Methodik der Fernerkundung SAR-Signalverarbeitung |
description |
This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which 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, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types |
format |
Article in Journal/Newspaper |
author |
Ressel, Rudolf Singha, Suman |
author_facet |
Ressel, Rudolf Singha, Suman |
author_sort |
Ressel, Rudolf |
title |
Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification |
title_short |
Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification |
title_full |
Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification |
title_fullStr |
Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification |
title_full_unstemmed |
Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification |
title_sort |
comparing near coincident space borne c and x band fully polarimetric sar data for arctic sea ice classification |
publisher |
Multidisciplinary Digital Publishing Institute (MDPI) |
publishDate |
2016 |
url |
https://elib.dlr.de/98218/ https://elib.dlr.de/98218/1/remotesensing-08-00198-v3.pdf http://www.mdpi.com/2072-4292/8/3/198 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_relation |
https://elib.dlr.de/98218/1/remotesensing-08-00198-v3.pdf Ressel, Rudolf und Singha, Suman (2016) Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification. Remote Sensing, 8 (3), Seiten 1-27. Multidisciplinary Digital Publishing Institute (MDPI). doi:10.3390/rs8030198 <https://doi.org/10.3390/rs8030198>. ISSN 2072-4292. |
op_doi |
https://doi.org/10.3390/rs8030198 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
3 |
container_start_page |
198 |
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1784262017943601152 |