[GRASS-user] Object-based image classification in GRASS
mlennert at club.worldonline.be
Wed Oct 30 13:04:22 PDT 2013
Based on the great work on i.segment by Eric and MarkusM, I've been
trying to put up a complete workflow allowing object-based image
classification in GRASS. Conclusion: it is possible with currently
available tools, even though some components would be nice to have in
addition. Attached you can find a simple shell script which shows all
the steps I went through. I commented it extensively, so it hopefully is
easy to understand.
- This only works in GRASS 7.
- It uses the v.class.mlpy addon module for classification, so that
needs to be installed. Kudos to Vaclav for that module ! It currently
only uses the DLDA classifier. The mlpy library offers many more, and I
think it should be quite easy to add them. Obviously, one could also
simply export the attribute table of the segments and of the training
areas to csv files and use R to do the classification.
- At the top of the script are a series of parameters that have to be
defined before being able to use the script as such (but the script is
more meant as a proof-of-concept than as a real script)
- Many other variables could be calculated for the segments: other
texture variables (possibly variables by segment, not as average of
pixel-based variables, cf ), other shape variables (cf the new work
of MarkusM on center lines and skeletons of polygons in v.voronoi), band
indices, etc. It would be interesting to hear what most people find useful.
- I do the step of digitizing training areas in the wxGUI digitizer
using the attribute editing tool and filling in the 'class' attribute
for those polygons I find representative. As already mentioned in
previous discussions , I do think that it would be nice if we could
have an attribute editing form that is independent of the vector digitizer.
More generally, it would be great to get feedback from interested people
on this approach to object-based image classification to see what we can
do to make it better.
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