dzetsaka : classification tool
dzetsaka 
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.
What does dzetsaka mean ?
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...
Discover dzetsaka
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.
Installation of scikit-learn
On Linux simply open terminal and type :
python3 -m pip install scikit-learn -U --user
On Windows
For QGIS 3.20 and higher: Open OsGeo shell, then :
o4w_env
python3 -m pip install scikit-learn -U --user
For Qgis 3.18 and lower: Open OsGeo shell, then :
py3_env.bat
python3 -m pip install scikit-learn -U --user
Thanks to Alexander Bruy for the tip.
For Qgis 2:
In the OsGeo setup, search for PIP and install it. Then you have few more steps to do. In the explorer, search for OsGeo4W Shell, right click to open it as an administrator. Now use pip in OsGeo Shell like on Linux. Just type :
pip install scikit-learn
If you do not have pip installed, open osgeo4w-setup-x86_64.exe, select Advanced install and install pip.
You can now use Random Forest, SVM, or KNN !
Tips
- 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.
Online dev documentation is available throught the doxygen branch.
Like us, use us ? Cite us !
If you use dzetsaka in your research and find it useful, please cite Dzetsaka using the following bibtex reference:
@misc{karasiak2016dzetsaka,
title={Dzetsaka Qgis Classification plugin},
author={Karasiak, Nicolas},
url={https://github.com/nkarasiak/dzetsaka},
year={2016},
doi={10.5281/zenodo.2552284}
}
Thanks to...
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.
