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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/43521 How to cite
Title: A sparsity-based variational approach for the restoration of SMOS images from L1A data
Authors: Preciozzi, Javier
Almansa, Andrés
Musé, Pablo
Durand, Sylvain
Khazaal, Ali
Rougé, Bernard
Type: Preprint
Keywords: SMOS, MIRAS, RFI, Brightness temperature, Non-differentiable convex optimization, Total variation minimization
Descriptors: Procesamiento de Señales
Issue Date: 2017
Abstract: 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.
Citation: 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
Academic department: Procesamiento de Señales
Investigation group: Tratamiento de Imágenes
License: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Appears in Collections:Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica

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