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

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