dzetsaka is very fast and easy to use but also a powerful classification plugin for Qgis. Initially based on Gaussian Mixture Model classifier developped by Mathieu Fauvel (now supports Random Forest, KNN and SVM), this plugin is a more generalist tool than Historical Map which was dedicated to classify forests from old maps. This plugin has by developped by Nicolas Karasiak.

A quick tutorial is available online (dzetsaka : how to make your first classification in qgis ?), or you can just download samples to test the plugin on your own.

As this tool was developped during my work in the Guiana Amazonian Park to classify different kind of vegetation, I gave an Teko name (a native-american language from a nation which lives in french Guiana) which represent the objects we use to see the world through, such as satellites, microscope, camera...

dzetsaka : Classification tool runs with scipy library. You can download package like Spider by Anaconda for a very easy setup.

Then, as this plugin is very simple, you will just need two things for making a good classification :

• A raster
• A shapefile which contains your ROI (Region Of Interest)

The shapefile must have a column which contains your classification numbers (1,3,4...). Otherwise if you use text or anything else it certainly won't work.

On Linux simply open terminal and type : pip install scikit-learn

On Windows, you have few more steps to do. Open Windows menu, and search for OsGeo4W Shell, open it as administrator (left click > open as an administrator), then type :
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

After get-pip.py has been downloaded write :
python get-pip.py

Now use pip in OsGeo Shell like on Linux. Just type :
pip install scikit-learn

You can now use Random Forest, SVM, or KNN !

• If your raster is spot6scene.tif, you can create your mask under the name spot6scene_mask.tif and the script will detect it automatically.
• If you want to keep your spectral ROI model from an image, you can save your model to use it on another image.

• Implement best progress bar for classifier like Random Forest / K-Nearest Neighbors.

Online dev documentation is available throught the doxygen branch.

I would like to thank the Guiana Amazonian Park for their trust in my work, and the Master 2 Geomatics Sigma for their excellent lessons in geomatics.