5 papers
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
Nicolas Gasnier, Emanuele Dalsasso, Loïc Denis +1
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy. Training the deep neural…
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
Emanuele Dalsasso, Inès Meraoumia, Loïc Denis +1
Deep learning approaches show unprecedented results for speckle reduction in SAR amplitude images. The wide availability of multi-temporal stacks of SAR images can improve even fur…
A review of deep-learning techniques for SAR image restoration
Loïc Denis, Emanuele Dalsasso, Florence Tupin
The speckle phenomenon remains a major hurdle for the analysis of SAR images. The development of speckle reduction methods closely follows methodological progress in the field of i…
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Emanuele Dalsasso, Xiangli Yang, Loïc Denis +2
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the…
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
Emanuele Dalsasso, Loïc Denis, Florence Tupin
Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the dif…