Improving a denoising strategy to restore linear structures
Encadrants : Florence Tupin, Emanuele Dalsasso
Email : email@example.com
Disponible : OUI
Spécialité : IMA
Nombre d'étudiants : 1
Description : SAR (Synthetic Aperture Radar) images have the advantage of being acquired at any time (day or night and whatever the weather conditions). However, they are very noisy because of the speckle noise due to coherent imaging. Deep networks have provided impressive speckle reduction results but still erase the fine dark linear structures corresponding to the road network.
Travail demandé : The aim of this project is to develop a strategy to help the network to preserve this thin information. To do so we propose to introduce the result of a line detector to modify the loss and constraint the network to better preserve the linear structures. Different strategies could be developed but a first step will be to train the network in a supervised setting  using a line detection algorithm  to weight the loss.
Liens complémentaires : References  Dalsasso, E., Yang, X., Denis, L., Tupin, F., & Yang, W. (2020). SAR image despeckling by deep neural networks: From a pre-trained model to an end-to-end training strategy. Remote Sensing, 12(16), 2636. https://www.mdpi.com/2072-4292/12/16/2636/pdf  Generalized Likelihood Ratio Tests for Linear Structure Detection in SAR Images Nicolas Gasnier, Loïc Denis, Florence Tupin EUSAR 2021: 13th European Conference on Synthetic Aperture Radar, Mar 2021, Leipzig (virtual), Germany  SAR2SAR: a semi-supervised despeckling algorithm for SAR images Emanuele Dalsasso, Loïc Denis, Florence Tupin IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, In press, pp.1-1. ⟨10.1109/JSTARS.2021.3071864⟩