159 citations · 527 across the 9 of their papers we have counts for
5 papers · 1 filter
As if by magic: self-supervised training of deep despeckling networks with MERLIN
Emanuele Dalsasso, Loïc Denis, Florence Tupin
Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. Speckle reduction has thus been the subject of numerous works spanning at least…
Image Restoration for Remote Sensing: Overview and Toolbox
Benhood Rasti, Yi Chang, Emanuele Dalsasso +2
Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) mode…
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…