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Open AccessArticle

Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

Institut des Géosciences de l’Environnement (IGE), Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, France
Oceanography and Global Change, Institut Mediterrani d’Estudis Avançats (IMEDEA) (CSIC-UIB), 07190 Esporles, Spain
Institut de Mathématiques de Bordeaux (IMB), Univ. Bordeaux, Bordeaux INP, CNRS, UMR 5251, F-33400 Talence, France
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 734;
Received: 26 November 2019 / Revised: 7 February 2020 / Accepted: 13 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Calibration and Validation of Satellite Altimetry)
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 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 10 4 and 10 3 for summer and winter, respectively. This is encouraging for the processing of the future SWOT data. View Full-Text
Keywords: SWOT; de-noising; variational regularization; western mediterranean SWOT; de-noising; variational regularization; western mediterranean
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Gómez-Navarro, L.; Cosme, E.; Sommer, J.L.; Papadakis, N.; Pascual, A. Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission. Remote Sens. 2020, 12, 734.

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