[GRASS-dev] Object-based image classification in GRASS

Moritz Lennert 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.

Some remarks:

- 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 [1]), 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 [2], 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.


[1] https://trac.osgeo.org/grass/ticket/2111
[2] http://lists.osgeo.org/pipermail/grass-dev/2013-February/062148.html
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