A sparsity-based variational approach for the restoration of SMOS images from L1A data
Resumen:
The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image T that models the Earth’s brightness temperature and an image O modeling the RFIs.
| 2017 | |
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SMOS MIRAS RFI Brightness temperature Non-differentiable convex optimization Total variation minimization Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/43521 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522942143168512 |
|---|---|
| author | Preciozzi, Javier |
| author2 | Almansa, Andrés Musé, Pablo Durand, Sylvain Khazaal, Ali Rougé, Bernard |
| author2_role | author author author author author |
| author_facet | Preciozzi, Javier Almansa, Andrés Musé, Pablo Durand, Sylvain Khazaal, Ali Rougé, Bernard |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Preciozzi, Javier Almansa, Andrés Musé, Pablo Durand, Sylvain Khazaal, Ali Rougé, Bernard |
| dc.date.accessioned.none.fl_str_mv | 2024-04-16T16:21:11Z |
| dc.date.available.none.fl_str_mv | 2024-04-16T16:21:11Z |
| dc.date.issued.es.fl_str_mv | 2017 |
| dc.date.submitted.es.fl_str_mv | 20240416 |
| dc.description.abstract.none.fl_txt_mv | The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image T that models the Earth’s brightness temperature and an image O modeling the RFIs. |
| dc.identifier.citation.es.fl_str_mv | Preciozzi, J, Almansa, A, Musé, P, Durand, S, Khazaal, A, Rougé, B. "A sparsity-based variational approach for the restoration of SMOS images from L1A data" [Preprint] Publicado en: IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2811-2826, May 2017, doi: 10.1109/TGRS.2017.2654864 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/43521 |
| 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 Brightness temperature Non-differentiable convex optimization Total variation minimization |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image T that models the Earth’s brightness temperature and an image O modeling the RFIs. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_d143bd422f030c0c5e6852dc788e19b9 |
| identifier_str_mv | Preciozzi, J, Almansa, A, Musé, P, Durand, S, Khazaal, A, Rougé, B. "A sparsity-based variational approach for the restoration of SMOS images from L1A data" [Preprint] Publicado en: IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2811-2826, May 2017, doi: 10.1109/TGRS.2017.2654864 |
| 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:20.500.12008/43521 |
| publishDate | 2017 |
| 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 | 2024-04-16T16:21:11Z2024-04-16T16:21:11Z201720240416Preciozzi, J, Almansa, A, Musé, P, Durand, S, Khazaal, A, Rougé, B. "A sparsity-based variational approach for the restoration of SMOS images from L1A data" [Preprint] Publicado en: IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2811-2826, May 2017, doi: 10.1109/TGRS.2017.2654864https://hdl.handle.net/20.500.12008/43521The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image T that models the Earth’s brightness temperature and an image O modeling the RFIs.Made available in DSpace on 2024-04-16T16:21:11Z (GMT). No. of bitstreams: 5 PAMDKR17.pdf: 9399535 bytes, checksum: 89a6ff19a7e1852a8983d31a4892b13c (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: 2017enengLas 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)SMOSMIRASRFIBrightness temperatureNon-differentiable convex optimizationTotal variation minimizationProcesamiento de SeñalesA sparsity-based variational approach for the restoration of SMOS images from L1A dataPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaPreciozzi, JavierAlmansa, AndrésMusé, PabloDurand, SylvainKhazaal, AliRougé, BernardProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | A sparsity-based variational approach for the restoration of SMOS images from L1A data Preciozzi, Javier SMOS MIRAS RFI Brightness temperature Non-differentiable convex optimization Total variation minimization Procesamiento de Señales |
| status_str | submittedVersion |
| title | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| title_full | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| title_fullStr | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| title_full_unstemmed | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| title_short | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| title_sort | A sparsity-based variational approach for the restoration of SMOS images from L1A data |
| topic | SMOS MIRAS RFI Brightness temperature Non-differentiable convex optimization Total variation minimization Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/43521 |
