SMOS images restoration from L1A data : a sparsity-based variational approach
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.
2014 | |
SMOS MIRAS RFI Non-differentiable Convex optimization Total variation minimization Procesamiento de Señales |
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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 |
format | conferenceObject |
id | COLIBRI_74f9f526ec30391b40a42c846c4a0f91 |
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 |
language_invalid_str_mv | en |
network_acronym_str | COLIBRI |
network_name_str | COLIBRI |
oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41823 |
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 |