SMOS images restoration from L1A data: A sparsity-based variational approach

Freciozzi, J.; Muse, P.; Almansa, A.; Durand, S.; Khazaal, A.; Rouge, B.

doi:10.1109/IGARSS.2014.6946977

by J. Freciozzi, P. Muse, A. Almansa, S. Durand, A. Khazaal, B. Rouge

Abstract:

Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image u that models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.

Reference:

SMOS images restoration from L1A data: A sparsity-based variational approach (J. Freciozzi, P. Muse, A. Almansa, S. Durand, A. Khazaal, B. Rouge), In 2014 IEEE Geoscience and Remote Sensing Symposium, IEEE, 2014.

Bibtex Entry:

@inproceedings{Preciozzi2014,
	Abstract = {Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image u that models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.},
	Address = {Quebec, Canada},
	Author = {Freciozzi, J. and Muse, P. and Almansa, A. and Durand, S. and Khazaal, A. and Rouge, B.},
	Booktitle = {2014 IEEE Geoscience and Remote Sensing Symposium},
	Doi = {10.1109/IGARSS.2014.6946977},
	Isbn = {978-1-4799-5775-0},
	Keywords = {Brightness temperature,Image resolution,Image restoration,L1a data,MIRAS,Minimization,Noise,RFI,SMOS,SMOS image restoration,Temperature measurement,brightness temperature,data degradation,geophysical image processing,image restoration,non-differentiable convex optimization,radio frequency interference,radiofrequency interference,remote sensing,sparsity based variational approach,total variation minimization,variational techniques},
	Month = {jul},
	Pages = {2487--2490},
	Publisher = {IEEE},
	Shorttitle = {Geoscience and Remote Sensing Symposium (IGARSS),},
	Title = {{SMOS images restoration from L1A data: A sparsity-based variational approach}},
	Year = {2014},
	Bdsk-Url-1 = {https://doi.org/10.1109/IGARSS.2014.6946977}}