Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar
Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea icemonitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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Online Access: | https://elib.dlr.de/98219/ https://elib.dlr.de/98219/1/Dual_Pol%20Sea%20Ice_JSTARS.pdf https://doi.org/10.1109/JSTARS.2016.2539501 |
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ftdlr:oai:elib.dlr.de:98219 2023-12-03T10:30:08+01:00 Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar Ressel, Rudolf Singha, Suman Lehner, Susanne Rösel, Anja Spreen, Gunnar 2016-08-02 application/pdf https://elib.dlr.de/98219/ https://elib.dlr.de/98219/1/Dual_Pol%20Sea%20Ice_JSTARS.pdf https://doi.org/10.1109/JSTARS.2016.2539501 de ger IEEE - Institute of Electrical and Electronics Engineers https://elib.dlr.de/98219/1/Dual_Pol%20Sea%20Ice_JSTARS.pdf Ressel, Rudolf und Singha, Suman und Lehner, Susanne und Rösel, Anja und Spreen, Gunnar (2016) Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (7), Seiten 3131-3143. IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/JSTARS.2016.2539501 <https://doi.org/10.1109/JSTARS.2016.2539501>. ISSN 1939-1404. SAR-Signalverarbeitung Institut für Methodik der Fernerkundung Zeitschriftenbeitrag PeerReviewed 2016 ftdlr https://doi.org/10.1109/JSTARS.2016.2539501 2023-11-06T00:23:49Z Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea icemonitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH–VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to bemore useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use. Article in Journal/Newspaper Sea ice German Aerospace Center: elib - DLR electronic library IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 7 3131 3143 |
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Open Polar |
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German Aerospace Center: elib - DLR electronic library |
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ftdlr |
language |
German |
topic |
SAR-Signalverarbeitung Institut für Methodik der Fernerkundung |
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SAR-Signalverarbeitung Institut für Methodik der Fernerkundung Ressel, Rudolf Singha, Suman Lehner, Susanne Rösel, Anja Spreen, Gunnar Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar |
topic_facet |
SAR-Signalverarbeitung Institut für Methodik der Fernerkundung |
description |
Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea icemonitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH–VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to bemore useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use. |
format |
Article in Journal/Newspaper |
author |
Ressel, Rudolf Singha, Suman Lehner, Susanne Rösel, Anja Spreen, Gunnar |
author_facet |
Ressel, Rudolf Singha, Suman Lehner, Susanne Rösel, Anja Spreen, Gunnar |
author_sort |
Ressel, Rudolf |
title |
Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar |
title_short |
Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar |
title_full |
Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar |
title_fullStr |
Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar |
title_full_unstemmed |
Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar |
title_sort |
investigation into different polarimetric features for sea ice classification using x-band synthetic aperture radar |
publisher |
IEEE - Institute of Electrical and Electronics Engineers |
publishDate |
2016 |
url |
https://elib.dlr.de/98219/ https://elib.dlr.de/98219/1/Dual_Pol%20Sea%20Ice_JSTARS.pdf https://doi.org/10.1109/JSTARS.2016.2539501 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://elib.dlr.de/98219/1/Dual_Pol%20Sea%20Ice_JSTARS.pdf Ressel, Rudolf und Singha, Suman und Lehner, Susanne und Rösel, Anja und Spreen, Gunnar (2016) Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (7), Seiten 3131-3143. IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/JSTARS.2016.2539501 <https://doi.org/10.1109/JSTARS.2016.2539501>. ISSN 1939-1404. |
op_doi |
https://doi.org/10.1109/JSTARS.2016.2539501 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
9 |
container_issue |
7 |
container_start_page |
3131 |
op_container_end_page |
3143 |
_version_ |
1784255818682597376 |