#acl BecariosGrupo:delete,read,write,admin All:read = 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. ([[http://ieeexplore.ieee.org/abstract/document/8052593/|link]])<
> == Download == [[attachment:JavierAndresRedolfi/sartb/sartb.tar.gz|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) {{{#!bash # apt-get install python2.7 python-matplotlib python-numpy python-skimage python-sklearn python-scipy }}} * Install vrl using the README provided by the library. {{{#!bash # 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: {{{#!bash $python src/bin/comparison_mueller.py --dataset=path_to_dataset }}}