A Sparsity-Based Variational Approach for the Restoration of SMOS Images From L1A Data

Preciozzi, Javier; Almansa, Andrés; Musé, Pablo; Durand, Sylvain; Khazaal, Ali; Rougé, Bernard

doi:10.1109/TGRS.2017.2654864

by Javier Preciozzi, Andrés Almansa, Pablo Musé, Sylvain Durand, Ali Khazaal, Bernard Rougé

Abstract:

The SMOS mission senses ocean salinity and soil moisture by measuring Earth's brightness temperature using in-terferometry 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 cause radio frequency interference (RFI) that mask the energy radiated from the Earth and strongly corrupt the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational model to recover super-resolved, 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. Experiments with synthetic and real data support the suitability of the proposed approach.

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Reference:

A Sparsity-Based Variational Approach for the Restoration of SMOS Images From L1A Data (Javier Preciozzi, Andrés Almansa, Pablo Musé, Sylvain Durand, Ali Khazaal, Bernard Rougé), In IEEE Transactions Geosciences and Remote Sensing, volume 55, 2017.

Bibtex Entry:

@article{preciozzi:hal-01341839,
	Abstract = {The SMOS mission senses ocean salinity and soil moisture by measuring Earth's brightness temperature using in-terferometry 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 cause radio frequency interference (RFI) that mask the energy radiated from the Earth and strongly corrupt the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational model to recover super-resolved, 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. Experiments with synthetic and real data support the suitability of the proposed approach.},
	Author = {Preciozzi, Javier and Almansa, Andr{\'{e}}s and Mus{\'{e}}, Pablo and Durand, Sylvain and Khazaal, Ali and Roug{\'{e}}, Bernard},
	Date-Added = {2017-04-20 21:42:11 +0000},
	Date-Modified = {2017-04-20 21:42:11 +0000},
	Doi = {10.1109/TGRS.2017.2654864},
	File = {:Users/almansa/Documents/Mendeley Desktop/PDFs/Preciozzi et al. - 2017 - A Sparsity-Based Variational Approach for the Restoration of SMOS Images From L1A Data.pdf:pdf},
	Issn = {0196-2892},
	Journal = {IEEE Transactions Geosciences and Remote Sensing},
	Keywords = {Environmental radiation effects,Image restoration,Image sensors,Inverse problems,Optimization methods,Radio Interferometry,Synthetic aperture imaging},
	Mendeley-Groups = {{\_}Proposals/2017-MAP5/almansa},
	Month = {feb},
	Number = {5},
	Pages = {2811--2826},
	Title = {{A Sparsity-Based Variational Approach for the Restoration of SMOS Images From L1A Data}},
	Url = {https://hal.archives-ouvertes.fr/hal-01341839/},
	Volume = {55},
	Year = {2017},
	Bdsk-Url-1 = {https://hal.archives-ouvertes.fr/hal-01341839/},
	Bdsk-Url-2 = {http://dx.doi.org/10.1109/TGRS.2017.2654864}}