[GRASS-user] supervised image classification using textural aspects

[Wout Bijkerk]

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