/Speckle_reduction_in_SAR_time_series_using_an_updating_strategy

Speckle reduction in SAR time series using an updating strategy, final project of the course "Remote sensing data: from sensor to large-scale geospatial data exploitation" by F.Tupin, G.Facciolo, E.Dalsasso, C.De Franchis, E.Meinhardt of the Master MVA (Mathematiques, Vision, Apprentissage)

Primary LanguageJupyter Notebook

Speckle_reduction_in_SAR_time_series_using_an_updating_strategy

Speckle reduction in SAR time series using an updating strategy, final project of the course "Remote sensing data: from sensor to large-scale geospatial data exploitation" by F.Tupin, G.Facciolo, E.Dalsasso, C.De Franchis, E.Meinhardt of the Master MVA (Mathematiques, Vision, Apprentissage)

Files :

  • Test_models.ipynb contain all tests of our work and plots.
  • Data_base_ratio_creation.ipynb is a notebook to create the Database of ratio images
  • Dataset.py, GenerateDataset.py, model.py are files useful to create neurone network models
  • models contain the pre-trained weights of models useful for our work. model.pth = SARtoSAR / model_ratio.pth = SARratiotoSARratio
  • data contain the SAR images useful for our work