Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission
In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/4/734 |
| _version_ | 1818870589726654464 |
|---|---|
| author | Laura Gómez-Navarro Emmanuel Cosme Julien Le Sommer Nicolas Papadakis Ananda Pascual |
| author_facet | Laura Gómez-Navarro Emmanuel Cosme Julien Le Sommer Nicolas Papadakis Ananda Pascual |
| author_sort | Laura Gómez-Navarro |
| collection | DOAJ |
| description | In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>∘</mo> </msup> </semantics> </math> </inline-formula> simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of <inline-formula> <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>3</mn> </msup> </semantics> </math> </inline-formula> for summer and winter, respectively. This is encouraging for the processing of the future SWOT data. |
| first_indexed | 2024-12-19T12:09:26Z |
| format | Article |
| id | doaj.art-2a388a8aef31413496ffb5b834b1ba84 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-19T12:09:26Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2a388a8aef31413496ffb5b834b1ba842022-12-21T20:22:15ZengMDPI AGRemote Sensing2072-42922020-02-0112473410.3390/rs12040734rs12040734Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite MissionLaura Gómez-Navarro0Emmanuel Cosme1Julien Le Sommer2Nicolas Papadakis3Ananda Pascual4Institut des Géosciences de l’Environnement (IGE), Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, FranceInstitut des Géosciences de l’Environnement (IGE), Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, FranceInstitut des Géosciences de l’Environnement (IGE), Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, FranceInstitut de Mathématiques de Bordeaux (IMB), Univ. Bordeaux, Bordeaux INP, CNRS, UMR 5251, F-33400 Talence, FranceOceanography and Global Change, Institut Mediterrani d’Estudis Avançats (IMEDEA) (CSIC-UIB), 07190 Esporles, SpainIn the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>∘</mo> </msup> </semantics> </math> </inline-formula> simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of <inline-formula> <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>3</mn> </msup> </semantics> </math> </inline-formula> for summer and winter, respectively. This is encouraging for the processing of the future SWOT data.https://www.mdpi.com/2072-4292/12/4/734swotde-noisingvariational regularizationwestern mediterranean |
| spellingShingle | Laura Gómez-Navarro Emmanuel Cosme Julien Le Sommer Nicolas Papadakis Ananda Pascual Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission Remote Sensing swot de-noising variational regularization western mediterranean |
| title | Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission |
| title_full | Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission |
| title_fullStr | Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission |
| title_full_unstemmed | Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission |
| title_short | Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission |
| title_sort | development of an image de noising method in preparation for the surface water and ocean topography satellite mission |
| topic | swot de-noising variational regularization western mediterranean |
| url | https://www.mdpi.com/2072-4292/12/4/734 |
| work_keys_str_mv | AT lauragomeznavarro developmentofanimagedenoisingmethodinpreparationforthesurfacewaterandoceantopographysatellitemission AT emmanuelcosme developmentofanimagedenoisingmethodinpreparationforthesurfacewaterandoceantopographysatellitemission AT julienlesommer developmentofanimagedenoisingmethodinpreparationforthesurfacewaterandoceantopographysatellitemission AT nicolaspapadakis developmentofanimagedenoisingmethodinpreparationforthesurfacewaterandoceantopographysatellitemission AT anandapascual developmentofanimagedenoisingmethodinpreparationforthesurfacewaterandoceantopographysatellitemission |