SELF-SUPERVISED LEARNING OF MULTI-MODAL COOPERATION FOR SAR DESPECKLING - Equipe Image, Modélisation, Analyse, GEométrie, Synthèse
Communication Dans Un Congrès Année : 2024

SELF-SUPERVISED LEARNING OF MULTI-MODAL COOPERATION FOR SAR DESPECKLING

Résumé

Synthetic aperture radar (SAR) is a widely used modality for Earth observation, as they provide weather-independent imaging capabilities. However, interpretation of SAR images is difficult due to the speckle phenomenon: fluctuations appear in the image, which are stronger in areas with high radar reflectivity. As a result, many speckle reduction methods have been developed, with deep learning approaches standing out as particularly effective. Our article presents here a deep learning approach with two novel features: the use of an optical image to improve the restoration of a SAR image, while using a self-supervised neural network training
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Dates et versions

hal-04676452 , version 1 (23-08-2024)

Identifiants

  • HAL Id : hal-04676452 , version 1

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Victor Gaya, Emanuele Dalsasso, Loïc Denis, Florence Tupin, Béatrice Pinel-Puysségur, et al.. SELF-SUPERVISED LEARNING OF MULTI-MODAL COOPERATION FOR SAR DESPECKLING. IGARSS, Jul 2024, Athenes, Grece, Greece. ⟨hal-04676452⟩
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