SMOS images restoration from L1A data : a sparsity-based variational approach

Preciozzi, Javier - Musé, Pablo - Almansa, Andrés - Durand, Sylvain - Khazaal, Ali - Rougé, Bernard

Resumen:

Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image uthat models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.


Detalles Bibliográficos
2014
SMOS
MIRAS
RFI
Non-differentiable
Convex optimization
Total variation minimization
Procesamiento de Señales
Inglés
Universidad de la República
COLIBRI
https://hdl.handle.net/20.500.12008/41823
Acceso abierto
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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author Preciozzi, Javier
author2 Musé, Pablo
Almansa, Andrés
Durand, Sylvain
Khazaal, Ali
Rougé, Bernard
author2_role author
author
author
author
author
author_facet Preciozzi, Javier
Musé, Pablo
Almansa, Andrés
Durand, Sylvain
Khazaal, Ali
Rougé, Bernard
author_role author
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collection COLIBRI
dc.creator.none.fl_str_mv Preciozzi, Javier
Musé, Pablo
Almansa, Andrés
Durand, Sylvain
Khazaal, Ali
Rougé, Bernard
dc.date.accessioned.none.fl_str_mv 2023-12-11T19:57:56Z
dc.date.available.none.fl_str_mv 2023-12-11T19:57:56Z
dc.date.issued.es.fl_str_mv 2014
dc.date.submitted.es.fl_str_mv 20231211
dc.description.abstract.none.fl_txt_mv Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image uthat models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.
dc.description.es.fl_txt_mv Trabajo aceptado en Geoscience and Remote Sensing Symposium, Quebec, Canada, 13-18 jul., 2014
dc.identifier.citation.es.fl_str_mv Freciozzi, J, Musé, P, Almansa, A, Durand, S, Khazaal, A, Rougé, B, "SMOS images restoration from L1A data : a sparsity-based variational approach" Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec, Canada, 13-18 jul, 2014, pp. 2487-2490, doi: 10.1109/IGARSS.2014.6946977.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12008/41823
dc.language.iso.none.fl_str_mv en
eng
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.subject.es.fl_str_mv SMOS
MIRAS
RFI
Non-differentiable
Convex optimization
Total variation minimization
dc.subject.other.es.fl_str_mv Procesamiento de Señales
dc.title.none.fl_str_mv SMOS images restoration from L1A data : a sparsity-based variational approach
dc.type.es.fl_str_mv Ponencia
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
description Trabajo aceptado en Geoscience and Remote Sensing Symposium, Quebec, Canada, 13-18 jul., 2014
eu_rights_str_mv openAccess
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identifier_str_mv Freciozzi, J, Musé, P, Almansa, A, Durand, S, Khazaal, A, Rougé, B, "SMOS images restoration from L1A data : a sparsity-based variational approach" Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec, Canada, 13-18 jul, 2014, pp. 2487-2490, doi: 10.1109/IGARSS.2014.6946977.
instacron_str Universidad de la República
institution Universidad de la República
instname_str Universidad de la República
language eng
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publishDate 2014
reponame_str COLIBRI
repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository_id_str 4771
rights_invalid_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
spelling 2023-12-11T19:57:56Z2023-12-11T19:57:56Z201420231211Freciozzi, J, Musé, P, Almansa, A, Durand, S, Khazaal, A, Rougé, B, "SMOS images restoration from L1A data : a sparsity-based variational approach" Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec, Canada, 13-18 jul, 2014, pp. 2487-2490, doi: 10.1109/IGARSS.2014.6946977.https://hdl.handle.net/20.500.12008/41823Trabajo aceptado en Geoscience and Remote Sensing Symposium, Quebec, Canada, 13-18 jul., 2014Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image uthat models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.Made available in DSpace on 2023-12-11T19:57:56Z (GMT). No. of bitstreams: 5 SMOS.pdf: 1429358 bytes, checksum: c4c9e293c58fef43a400cd6fae576440 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2014enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)SMOSMIRASRFINon-differentiableConvex optimizationTotal variation minimizationProcesamiento de SeñalesSMOS images restoration from L1A data : a sparsity-based variational approachPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaPreciozzi, JavierMusé, PabloAlmansa, AndrésDurand, SylvainKhazaal, AliRougé, BernardProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse
spellingShingle SMOS images restoration from L1A data : a sparsity-based variational approach
Preciozzi, Javier
SMOS
MIRAS
RFI
Non-differentiable
Convex optimization
Total variation minimization
Procesamiento de Señales
status_str publishedVersion
title SMOS images restoration from L1A data : a sparsity-based variational approach
title_full SMOS images restoration from L1A data : a sparsity-based variational approach
title_fullStr SMOS images restoration from L1A data : a sparsity-based variational approach
title_full_unstemmed SMOS images restoration from L1A data : a sparsity-based variational approach
title_short SMOS images restoration from L1A data : a sparsity-based variational approach
title_sort SMOS images restoration from L1A data : a sparsity-based variational approach
topic SMOS
MIRAS
RFI
Non-differentiable
Convex optimization
Total variation minimization
Procesamiento de Señales
url https://hdl.handle.net/20.500.12008/41823