A sparsity-based variational approach for the restoration of SMOS images from L1A data

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
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  • a comme partie hal-01265931 - Ceci est une version longue (journal) généralisant deux publications de conférence présentées à IGARSS 2012 et IGARSS 2014.

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Javier Preciozzi, Andrès Almansa, Pablo Musé, Sylvain Durand, Ali Khazaal, et al.. A sparsity-based variational approach for the restoration of SMOS images from L1A data. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (5), pp.2811--2826. 〈http://ieeexplore.ieee.org/document/7858753/〉. 〈10.1109/TGRS.2017.2654864〉. 〈hal-01341839v2〉

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