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ADL_SARdespeckling

Python workflow to remove speckle nosie from C-Band SAR images

Description

The basic workflow setup includes the following modules and tasks:

  • run: modul to run function via command line interface
  • preprocessing: preperation of the training data
    • add_speckle_noise: Function to add simulated speckle noise to SAR data
    • patch_extractor: Extracts image square shaped patches from input images and writes them to file
  • model: Function defining the Residual U-Net Model
  • train:
    • get_model: Helper function creating a U-Net model instance with the given parameters.
    • train_model: Function to train Residual U-Net Despeckling model using the provided parameters
    • make_prediction: Function to make prediction using the input path for a pretrained Residual U-Net Despeckling model
  • postprocessing: postprocessing to evaluate the predictions and reconstruct predicted data
    • despeckle_sar_image: Function to merge patches to the extent of the original SAR scene and georeference it
    • diff_image: Function to calculte the difference in dB between to given images
  • utils: Provides class to load and feed data to the model
    • DataGenerator: Class that generates data for Keras Sequence based data generator

Installation

To install adl_sardespeckling, you can create a conda environment, if no appropriate environment is in place yet:

.. code-block:: bash

conda create --name adl_sardespeckling -c conda-forge python=3.6 gdal=3.0.2
conda activate adl_sardespeckling

ATTENTION: It is expected that the existing environment has already a working GDAL installation. GDAL needs more OS support and has more dependencies then other packages and can therefore not be installed solely via pip. Please have a look at https://pypi.org/project/GDAL/ what requirements are needed. Thus, for a fresh setup, an existing environment with python=3.6 and gdal=3.0.2 are expected.

The last installation step involves the setting of the required environmental variables for GDAL.

.. code-block:: bash

export PROJ_LIB="[...]/miniconda/envs/adl_sardespeckling/share/proj"
export GDAL_DATA="[...]/miniconda/envs/adl_sardespeckling/share/gdal"

Data

As the input SAR images are very large and exceed the allowed file size, they are not added to the repository.

Model training

To apply your own or the pre-trained model to a SAR image call the following function from th run.py modul -function train --train_path your_input_training_data_path --reference_path your_reference_data_path --batch_size your_batch_size --steps_per_epochs your_steps_peer_epoch --patch_size your_patch_size --n_channels your_number_of_channels --epochs your_numbere_of_epochs --save_model true/false

Model prediction

To make predictions for image patches on a pre_trained model call the following function from th run.py modul -function predict --path2input your_input_path --path2model your_model_path --outpath your_output_path

Depseckling SAR image

To apply your own or the pre-trained model to a SAR image call the following function from th run.py modul -function despeckle --input_path your_input_path --output_path your_output_path --path2model your_model_path --overlay e.g. 40

Note

The workplan as well as a log book describing the current status of the proect, open issues and the next steps can be found here: https://github.com/FelixReuss/ADL_SARdespeckling/blob/main/ADL_Exercise2-1431542.pdf

This project has been set up using PyScaffold 3.3.1. For details and usage information on PyScaffold see https://pyscaffold.org/.