[GRASS-user] supervised image classification using textural
wout.bijkerk at xs4all.nl
Mon Aug 25 17:22:33 EDT 2008
Markus (and others)
Answering my question
> 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?
Markus Neteler wrote
> It's worth trying. I have done so for orthophoto classification and
> it definitely helped:
> The texture map(s) add patterns which can stabilize the process
> of assigning pixels to classes/can improve the segmentation part
> of i.smap as it renders heterogeneous areas to homogeneous
Thanks for your tips.
The explanation of r.texture in the manual pages has puzzled me, and in
fact partly it still does. At first I had the impression r.texture was
just a slight modification of r.neighbors, but after reading
I discovered that r.texture makes use of several quite advanced
algorithms. Now I can, at least partly, depict more or less what it
effectively is doing. The problem that I am stil facing, is which
textural measure might give the best input for i.smap. Anyway I will
give Sum Avarage a try. With my dataset (resolution 0,5 meters and
mainly smaller trees and shrubs), I will use a distance value of 2 and,
following your example, using two windowsize: a smal one of 3 and a
medium one of 9.
Untill now I used the Variance measure with distance 1 and windowsize 3.
I (not knowing what r.texture realy does) used only one direction as
input and the result (r,kappa) was worse than with no textural measure.
This was caused by i.e. transitions from water to grassland that (with
higher variance) were classified as forest
Jonathans bottom-up approach (
seems interesting. I might give that one a try for a more detailed
classification as a testcase, since we have also a detailed vegetation
map (scale 1:5000, dating from 2005) at hand that we use for training
sites in our coarser classification.
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