Fisher Vectors for PolSAR Image Classification

Code used in paper:

Javier Redolfi, Jorge Sánchez, and Ana Georgina Flesia
"Fisher Vectors for PolSAR Image Classification"
IEEE Geoscience and Remote Sensing Letters, 99, Septiembre 2017. (link)

Download

JavierAndresRedolfi/sartb/sartb.tar.gz

Features

The library consist basically of the following python modules:

• PolSAR image manipulation: pseudocolor image generation, force SPD.
• PolSAR datasets: San Francisco Bay, Flevoland and Foulum. Train/test splits generation, manual selection of training points. Accuracy and Confusion Matrix computation.
• Smoothing algorithms: Potts, Graph Cut and Maximun.
• Unary potential generation from Complex Wishart, Real Wishart Mixture and FV (RWM based).

Dependencies

• vrl -> http://www.famaf.unc.edu.ar/~jsanchez/efv/

• matplotlib
• numpy

• pygco -> http://vision.csd.uwo.ca/code/gco-v3.0.zip

• pystruct -> https://pystruct.github.io/

• scikit-image -> http://scikit-image.org/

• scikit-learn -> scikit-learn.org/

• scipy

Installation on Debian/Ubuntu

(use sudo for Ubuntu)

# apt-get install python2.7 python-matplotlib python-numpy python-skimage python-sklearn python-scipy

• Install vrl using the README provided by the library.

# pip install pystruct

• Install pygco using the gco_python/README.md provided by the library.

Installing the module

• Just uncompress it.

Example: Comparison with paper "Pol-SAR Classification Based on Generalized Polar Decomposition of Mueller Matrix"

• Download the dataset and uncompress it: -> https://www.dropbox.com/s/xulk8t5pizoceh5/dataset.tar.gz?dl=0

• Run the script:

$python src/bin/comparison_mueller.py --dataset=path_to_dataset