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readme.md

Processing Chains Comparator (PCC)

This tools allows the user to compare the output of cloud detection processing chains against a reference. The reference is created with ALCD beforehand. The current supported processing chains (PC) are MAJA, Sen2Cor and Fmask

This readme describes quickly the use of each python file, the general use of the tool is described in the user manual in the parent directory.

Description of each file

  • all_run_pcc.py main file, calls the other files. Run the full PCC code. Called by: /
  • masks_conversion.py used to convert the output from the 3 PC to the ALCD equivalent one. Called by: all_run_pcc.py
  • find_chain_directory_paths.py to search for the paths of the chains outputs. Called by: all_run_pcc.py
  • comparison.py compares the converted masks output by the PCs against the ALCD reference. Called by: all_run_pcc.py
  • metrics_grapher.py plots the metrics (accuracy, f1-score, recall, precision) for a scene, with the condition that the 3 PCs have been run on it. Called by: all_run_pcc.py
  • alcd_labellisation_posttreatment.py used to perform morphological operations on labeled GTiffs Called by: masks_conversion.py
  • png_converter.py converts Gtiffs to pngs, and adds a text to them (PC name, location and date of the scene) Called by: all_run_pcc.py
  • statistics_synthesis.py gives global statistics on all the scenes. Needs to be run independantly. Called by: /
  • pixels_features_analysis.py analyses the features values of the original image, given their ALCD reference class. It is now deprecated. Needs to be run independantly. Called by: all_run_pcc.py

Getting Started

You need to set the parameters as described in the user manual. Then you can simply run

python all_run_pcc.py -l Arles -d 20171002 -b True

Once you did it for all the scenes, you can aggregate the results with

python statistics_synthesis.py