176 citations · 785 across the 18 of their papers we have counts for
8 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…
REXPACO: an algorithm for high contrast reconstruction of the circumstellar environment by angular differential imaging
Olivier Flasseur, Samuel Thé, Loïc Denis +2
Aims. The purpose of this paper is to describe a new post-processing algorithm dedicated to the reconstruction of the spatial distribution of light received from off-axis sources,…
Urban Surface Reconstruction in SAR Tomography by Graph-Cuts
Clément Rambour, Loïc Denis, Florence Tupin +3
SAR (Synthetic Aperture Radar) tomography reconstructs 3-D volumes from stacks of SAR images. High-resolution satellites such as TerraSAR-X provide images that can be combined to p…
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…