- My favorite aspect of the session was the way you explain the classifiers. It was very helpful for me to understand it.
The best classification I got with training areas and ndvi, rabidEye band 4, landsat band 5 and landsat band 8 and the classifier maximum likelyhood. The only problem was with some water in the city where there is no water. I could not get my classification without this water.
I use r.kappa from processing tools in qGIS.
I use this site for help:
I made test area like training area and converted to raster for r.kappa. Then I used this raster and classification to get the kappa index.
Cohen’s Kappa index of agreement tests if two people/algorithms would make similar classification.
The value is very bad, but maybe it is because there is class with id 0, and this is not in my
classification. It is from the test area raster. I did not find how to tell qGIS to ignore 0.