Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods

In this study, dual-polarimetric (DP) Synthetic Aperture Radar (SAR) data collected by the RadarSAT-2 SAR mission in Antarctica are exploited to observe Terra Nova Bay (TNB) polynya, Ross Sea, Antarctica. Single-polarization segmentation and a dual-polarimetric classification scheme are adopted to a...

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Published in:2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)
Main Authors: Nunziata, F., Buono, A., Moctezuma-Flores, M., Parmiggiani, F., Migliaccio, M.
Format: Conference Object
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Subjects:
Online Access:http://hdl.handle.net/11367/65202
https://doi.org/10.1109/RTSI.2017.8065884
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author Nunziata, F.
Buono, A.
Moctezuma-Flores, M.
Parmiggiani, F.
Migliaccio, M.
author2 Nunziata, F.
Buono, A.
Moctezuma-Flores, M.
Parmiggiani, F.
Migliaccio, M.
author_facet Nunziata, F.
Buono, A.
Moctezuma-Flores, M.
Parmiggiani, F.
Migliaccio, M.
author_sort Nunziata, F.
collection Università degli Studi di Napoli "Parthenope": CINECA IRIS
container_start_page 1
container_title 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)
description In this study, dual-polarimetric (DP) Synthetic Aperture Radar (SAR) data collected by the RadarSAT-2 SAR mission in Antarctica are exploited to observe Terra Nova Bay (TNB) polynya, Ross Sea, Antarctica. Single-polarization segmentation and a dual-polarimetric classification scheme are adopted to analyze the polynya. Experimental results show that single-polarization segmentation allows correctly extracting polynya; while dual-polarimetric information provides additional information that allows a rough classification according to the dominant scattering mechanism that characterizes the observed scene.
format Conference Object
genre Antarc*
Antarctica
Ross Sea
genre_facet Antarc*
Antarctica
Ross Sea
geographic Ross Sea
Terra Nova Bay
geographic_facet Ross Sea
Terra Nova Bay
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3rd IEEE International Forum on Research and Technologies for Society and Industry, RTSI 2017
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spelling ftuninapoliparth:oai:ricerca.uniparthenope.it:11367/65202 2025-01-16T19:21:23+00:00 Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods Nunziata, F. Buono, A. Moctezuma-Flores, M. Parmiggiani, F. Migliaccio, M. Nunziata, F. Buono, A. Moctezuma-Flores, M. Parmiggiani, F. Migliaccio, M. 2017 http://hdl.handle.net/11367/65202 https://doi.org/10.1109/RTSI.2017.8065884 eng eng Institute of Electrical and Electronics Engineers Inc. info:eu-repo/semantics/altIdentifier/isbn/9781538639061 info:eu-repo/semantics/altIdentifier/wos/WOS:000426978000012 ispartofbook:RTSI 2017 - IEEE 3rd International Forum on Research and Technologies for Society and Industry, Conference Proceedings 3rd IEEE International Forum on Research and Technologies for Society and Industry, RTSI 2017 firstpage:1 lastpage:4 numberofpages:4 http://hdl.handle.net/11367/65202 doi:10.1109/RTSI.2017.8065884 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85034831414 Multi-polarization SAR image RadarSAT-2 Terra Nova Bay polynya Computer Networks and Communication Computer Science Applications1707 Computer Vision and Pattern Recognition Energy Engineering and Power Technology Industrial and Manufacturing Engineering Health (social science) Management of Technology and Innovation Artificial Intelligence info:eu-repo/semantics/conferenceObject 2017 ftuninapoliparth https://doi.org/10.1109/RTSI.2017.8065884 2024-03-28T01:12:57Z In this study, dual-polarimetric (DP) Synthetic Aperture Radar (SAR) data collected by the RadarSAT-2 SAR mission in Antarctica are exploited to observe Terra Nova Bay (TNB) polynya, Ross Sea, Antarctica. Single-polarization segmentation and a dual-polarimetric classification scheme are adopted to analyze the polynya. Experimental results show that single-polarization segmentation allows correctly extracting polynya; while dual-polarimetric information provides additional information that allows a rough classification according to the dominant scattering mechanism that characterizes the observed scene. Conference Object Antarc* Antarctica Ross Sea Università degli Studi di Napoli "Parthenope": CINECA IRIS Ross Sea Terra Nova Bay 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) 1 4
spellingShingle Multi-polarization SAR image
RadarSAT-2
Terra Nova Bay polynya
Computer Networks and Communication
Computer Science Applications1707 Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Industrial and Manufacturing Engineering
Health (social science)
Management of Technology and Innovation
Artificial Intelligence
Nunziata, F.
Buono, A.
Moctezuma-Flores, M.
Parmiggiani, F.
Migliaccio, M.
Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods
title Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods
title_full Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods
title_fullStr Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods
title_full_unstemmed Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods
title_short Observations of Terra Nova Bay polynya by RadarSAT-2: Dual-and single-polarization methods
title_sort observations of terra nova bay polynya by radarsat-2: dual-and single-polarization methods
topic Multi-polarization SAR image
RadarSAT-2
Terra Nova Bay polynya
Computer Networks and Communication
Computer Science Applications1707 Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Industrial and Manufacturing Engineering
Health (social science)
Management of Technology and Innovation
Artificial Intelligence
topic_facet Multi-polarization SAR image
RadarSAT-2
Terra Nova Bay polynya
Computer Networks and Communication
Computer Science Applications1707 Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Industrial and Manufacturing Engineering
Health (social science)
Management of Technology and Innovation
Artificial Intelligence
url http://hdl.handle.net/11367/65202
https://doi.org/10.1109/RTSI.2017.8065884