Texture-based sea ice classification on TerraSAR-X imagery
Sea ice monitoring has attracted growing attention over the last decade due to its importance in global warming. Besides the purely scientific interest, practical implications of global warming are the increased navigability of ice-infested sea passages such as the Arctic Northwestern and Northeaste...
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ftdlr:oai:elib.dlr.de:89495 2024-05-19T07:36:30+00:00 Texture-based sea ice classification on TerraSAR-X imagery Ressel, Rudolf Lehner, Susanne 2014 application/pdf https://elib.dlr.de/89495/ https://elib.dlr.de/89495/1/2014_Ressel_Lehner_ICE14-1243_final.pdf en eng Research Publishing Services, Singapur https://elib.dlr.de/89495/1/2014_Ressel_Lehner_ICE14-1243_final.pdf Ressel, Rudolf und Lehner, Susanne (2014) Texture-based sea ice classification on TerraSAR-X imagery. In: Proceedings of the 22 IAHR International Symposium on ICE 2014 (IAHR-ICE 2014), Seiten 503-509. Research Publishing Services, Singapur. IAHR-ICE 2014, 2014-08-11 - 2014-08-15, Singapur, Singapur. ISBN 978 981 09 0750 1. SAR-Signalverarbeitung Konferenzbeitrag PeerReviewed 2014 ftdlr 2024-04-25T00:29:41Z Sea ice monitoring has attracted growing attention over the last decade due to its importance in global warming. Besides the purely scientific interest, practical implications of global warming are the increased navigability of ice-infested sea passages such as the Arctic Northwestern and Northeastern passages. To assist maritime endeavors in these areas, ice type classification is pivotal. National sea ice surveillance services of several countries have provided ice charts on a continuous basis, mostly generated by human experts in a manual fashion. These classifications are based on a variety of data sources, mostly from microwave or optical spaceborne and airborne sources. In this paper we present an approach that relies on TerraSAR-X Satellite data. Such data offers images at a high resolution in a radar band so far very rarely applied for ice classification. In order to build on expert knowledge of the past, we designed an artificial neural network approach, which outputs a number of suitable ice type classes. Input neurons are fed by an automated feature extraction algorithm. These features are based on popular and wellestablished texture analysis methods, most notably graylevel co-occurrence matrices (GLCM) and local binary patterns (LBP). Images are acquired for a selected eographical area for which ground truth data can be obtained from national ice services (Baltic sea). From these datasets, training and validation samples are chosen and evaluated. Classification results are compared with official ice charts. We asses suitability for near real time services. Based on first examples and computed results, we conclude that our approach is rather promising for automatic near real time (NRT) services. Conference Object Arctic Global warming Sea ice German Aerospace Center: elib - DLR electronic library |
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Open Polar |
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German Aerospace Center: elib - DLR electronic library |
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language |
English |
topic |
SAR-Signalverarbeitung |
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SAR-Signalverarbeitung Ressel, Rudolf Lehner, Susanne Texture-based sea ice classification on TerraSAR-X imagery |
topic_facet |
SAR-Signalverarbeitung |
description |
Sea ice monitoring has attracted growing attention over the last decade due to its importance in global warming. Besides the purely scientific interest, practical implications of global warming are the increased navigability of ice-infested sea passages such as the Arctic Northwestern and Northeastern passages. To assist maritime endeavors in these areas, ice type classification is pivotal. National sea ice surveillance services of several countries have provided ice charts on a continuous basis, mostly generated by human experts in a manual fashion. These classifications are based on a variety of data sources, mostly from microwave or optical spaceborne and airborne sources. In this paper we present an approach that relies on TerraSAR-X Satellite data. Such data offers images at a high resolution in a radar band so far very rarely applied for ice classification. In order to build on expert knowledge of the past, we designed an artificial neural network approach, which outputs a number of suitable ice type classes. Input neurons are fed by an automated feature extraction algorithm. These features are based on popular and wellestablished texture analysis methods, most notably graylevel co-occurrence matrices (GLCM) and local binary patterns (LBP). Images are acquired for a selected eographical area for which ground truth data can be obtained from national ice services (Baltic sea). From these datasets, training and validation samples are chosen and evaluated. Classification results are compared with official ice charts. We asses suitability for near real time services. Based on first examples and computed results, we conclude that our approach is rather promising for automatic near real time (NRT) services. |
format |
Conference Object |
author |
Ressel, Rudolf Lehner, Susanne |
author_facet |
Ressel, Rudolf Lehner, Susanne |
author_sort |
Ressel, Rudolf |
title |
Texture-based sea ice classification on TerraSAR-X imagery |
title_short |
Texture-based sea ice classification on TerraSAR-X imagery |
title_full |
Texture-based sea ice classification on TerraSAR-X imagery |
title_fullStr |
Texture-based sea ice classification on TerraSAR-X imagery |
title_full_unstemmed |
Texture-based sea ice classification on TerraSAR-X imagery |
title_sort |
texture-based sea ice classification on terrasar-x imagery |
publisher |
Research Publishing Services, Singapur |
publishDate |
2014 |
url |
https://elib.dlr.de/89495/ https://elib.dlr.de/89495/1/2014_Ressel_Lehner_ICE14-1243_final.pdf |
genre |
Arctic Global warming Sea ice |
genre_facet |
Arctic Global warming Sea ice |
op_relation |
https://elib.dlr.de/89495/1/2014_Ressel_Lehner_ICE14-1243_final.pdf Ressel, Rudolf und Lehner, Susanne (2014) Texture-based sea ice classification on TerraSAR-X imagery. In: Proceedings of the 22 IAHR International Symposium on ICE 2014 (IAHR-ICE 2014), Seiten 503-509. Research Publishing Services, Singapur. IAHR-ICE 2014, 2014-08-11 - 2014-08-15, Singapur, Singapur. ISBN 978 981 09 0750 1. |
_version_ |
1799475636376436736 |