Hello everybody, I am trying to perform a supervised classification of false color images. The resolution of the bands (IR,R,G) is 1 meter. Additionally I can use a DEM as input ( hor. res. = 5m), but apparently null-values within the training areas are causing some problems (see http://lists.osgeo.org/pipermail/grass-user/2008-June/045261.html) so I am not using the DEM for the moment. I intend to use the combined radiometric and geometric modules i.gensigset and i.smap.
Looking at the images, I wonder if including textural features within the images would be usefull: a forest canopy has a far coarser texture than a grassland. Also in the Grassbook this is mentioned, and for the supervised classification of saltmarshes in Germany, textural features are also used (see i.e. http://www.nature-consult.de/images/downl/Agit_2008_nature-consult.pdf, but in German), but this is not further explained. This brings me to the following questions: 1) Is it usefull to make a raster with textural image features as an extra input for i.gensigset / i.smap? The i.gensigset / i.smap procedure is partly based on geometry and therefor on texture as well so what does a texturemap add? 2) if it is usefull, which textural feature is then aproppriate? I have been experimenting and until now simply variance seems to make the difference between forest and shrubland compared to grassland, and reed-vegetation. This was using a windowsize of 5, meaning 5x5 m. Did anyone have any experience with this? Regards, Wout _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
