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

J. Preciozzi (1) , Pablo Musé (1) , Andrés Almansa (2, 3) , Sylvain Durand (4) , Ali Khazaal (5) , Bernard Rougé (5)

1 UDELAR - Universidad de la República [Montevideo]
2 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
3 TSI - Département Traitement du Signal et des Images
4 UPD5 - Université Paris Descartes - Paris 5
5 CESBIO - Centre d'études spatiales de la biosphère

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.

Domains: Computer Science [cs]; Image Processing [eess.IV]; Computer Science [cs]; Numerical Analysis [cs.NA]; Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]; Engineering Sciences [physics]; Signal and Image processing

J. Preciozzi, Pablo Musé, Andrés Almansa, Sylvain Durand, Ali Khazaal, et al.. SMOS images restoration from L1A data: A sparsity-based variational approach. IEEE Geoscience and Remote Sensing Symposium, Jul 2014, Quebec, Canada. pp.2487-2490, ⟨10.1109/IGARSS.2014.6946977⟩. ⟨hal-01265931⟩