[GRASS-user] supervised image classification using textural aspects
wout.bijkerk at xs4all.nl
Fri Aug 22 08:29:21 EDT 2008
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.
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?
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