Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures
Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary t...
Published in: | 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |
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Main Authors: | , , |
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Format: | Conference Object |
Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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Online Access: | http://hdl.handle.net/2117/366675 https://doi.org/10.1109/IGARSS47720.2021.9554671 |
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ftupcatalunyair:oai:upcommons.upc.edu:2117/366675 |
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Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
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ftupcatalunyair |
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English |
topic |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar Remote sensing Bayesian statistical decision theory Synthetic aperture radar Sea ice Melt ponds Bayesian inference Sentinel-1 Synthetic Aperture Radar (SAR) Teledetecció Estadística bayesiana Radar d'obertura sintètica |
spellingShingle |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar Remote sensing Bayesian statistical decision theory Synthetic aperture radar Sea ice Melt ponds Bayesian inference Sentinel-1 Synthetic Aperture Radar (SAR) Teledetecció Estadística bayesiana Radar d'obertura sintètica Herbert, Christoph Josef Camps Carmona, Adriano José Vall-Llossera Ferran, Mercedes Magdalena Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures |
topic_facet |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar Remote sensing Bayesian statistical decision theory Synthetic aperture radar Sea ice Melt ponds Bayesian inference Sentinel-1 Synthetic Aperture Radar (SAR) Teledetecció Estadística bayesiana Radar d'obertura sintètica |
description |
Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary to discriminate intact sea ice from closed water. Important information about surface roughness conditions are contained in the distribution of radar backscattering images which can be - in principle - used to detect melt ponds and different sea ice types. In this work, a two-step probabilistic approach based on Expectation-Maximization and Bayesian inference considers the spatial and statistical characteristics of medium-resolution daily-available Sentinel-1 SAR images. The presented method segments sea ice into a number of separable classes and enables to discriminate surface water from the remaining sea ice types. The lead author was supported by “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/D118/11660050, and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. The project was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033. Peer Reviewed Postprint (published version) |
author2 |
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció |
format |
Conference Object |
author |
Herbert, Christoph Josef Camps Carmona, Adriano José Vall-Llossera Ferran, Mercedes Magdalena |
author_facet |
Herbert, Christoph Josef Camps Carmona, Adriano José Vall-Llossera Ferran, Mercedes Magdalena |
author_sort |
Herbert, Christoph Josef |
title |
Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures |
title_short |
Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures |
title_full |
Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures |
title_fullStr |
Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures |
title_full_unstemmed |
Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures |
title_sort |
probabilistic inference method to discriminate closed water from sea ice using sentinel-1 sar signatures |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
publishDate |
2021 |
url |
http://hdl.handle.net/2117/366675 https://doi.org/10.1109/IGARSS47720.2021.9554671 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://ieeexplore.ieee.org/document/9554671 info:eu-repo/grantAgreement/EC/H2020/713673/EU/Innovative doctoral programme for talented early-stage researchers in Spanish host organisations excellent in the areas of Science, Technology, Engineering and Mathematics (STEM)./INPhINIT info:eu-repo/grantAgreement/MINECO/1PE/MDM-2016-0600 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2017-89463-C3-2-R/ES/SOBRE LA CONTINUIDAD DE LAS MISIONES SATELITALES DE BANDA L: NUEVOS PARADIGMAS EN PRODUCTOS Y APLICACIONES/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099008-B-C21/ES/SENSING WITH PIONEERING OPPORTUNISTIC TECHNIQUES/ Herbert, C.; Camps, A.; Vall-llossera, M. Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures. A: IEEE International Geoscience and Remote Sensing Symposium. "IGARSS 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium: 12-16 July, 2021, virtual symposium, Brussels, Belgium: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 5546-5549. ISBN 978-1-6654-0369-6. DOI 10.1109/IGARSS47720.2021.9554671. 978-1-6654-0369-6 http://hdl.handle.net/2117/366675 doi:10.1109/IGARSS47720.2021.9554671 |
op_rights |
Open Access |
op_doi |
https://doi.org/10.1109/IGARSS47720.2021.9554671 |
container_title |
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |
container_start_page |
5546 |
op_container_end_page |
5549 |
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1810475998542561280 |
spelling |
ftupcatalunyair:oai:upcommons.upc.edu:2117/366675 2024-09-15T18:34:12+00:00 Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures Herbert, Christoph Josef Camps Carmona, Adriano José Vall-Llossera Ferran, Mercedes Magdalena Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció 2021 4 p. application/pdf http://hdl.handle.net/2117/366675 https://doi.org/10.1109/IGARSS47720.2021.9554671 eng eng Institute of Electrical and Electronics Engineers (IEEE) https://ieeexplore.ieee.org/document/9554671 info:eu-repo/grantAgreement/EC/H2020/713673/EU/Innovative doctoral programme for talented early-stage researchers in Spanish host organisations excellent in the areas of Science, Technology, Engineering and Mathematics (STEM)./INPhINIT info:eu-repo/grantAgreement/MINECO/1PE/MDM-2016-0600 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2017-89463-C3-2-R/ES/SOBRE LA CONTINUIDAD DE LAS MISIONES SATELITALES DE BANDA L: NUEVOS PARADIGMAS EN PRODUCTOS Y APLICACIONES/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099008-B-C21/ES/SENSING WITH PIONEERING OPPORTUNISTIC TECHNIQUES/ Herbert, C.; Camps, A.; Vall-llossera, M. Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures. A: IEEE International Geoscience and Remote Sensing Symposium. "IGARSS 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium: 12-16 July, 2021, virtual symposium, Brussels, Belgium: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 5546-5549. ISBN 978-1-6654-0369-6. DOI 10.1109/IGARSS47720.2021.9554671. 978-1-6654-0369-6 http://hdl.handle.net/2117/366675 doi:10.1109/IGARSS47720.2021.9554671 Open Access Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar Remote sensing Bayesian statistical decision theory Synthetic aperture radar Sea ice Melt ponds Bayesian inference Sentinel-1 Synthetic Aperture Radar (SAR) Teledetecció Estadística bayesiana Radar d'obertura sintètica Conference report 2021 ftupcatalunyair https://doi.org/10.1109/IGARSS47720.2021.9554671 2024-07-25T11:15:56Z Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary to discriminate intact sea ice from closed water. Important information about surface roughness conditions are contained in the distribution of radar backscattering images which can be - in principle - used to detect melt ponds and different sea ice types. In this work, a two-step probabilistic approach based on Expectation-Maximization and Bayesian inference considers the spatial and statistical characteristics of medium-resolution daily-available Sentinel-1 SAR images. The presented method segments sea ice into a number of separable classes and enables to discriminate surface water from the remaining sea ice types. The lead author was supported by “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/D118/11660050, and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. The project was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033. Peer Reviewed Postprint (published version) Conference Object Sea ice Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 5546 5549 |