Pietro,
On 31/07/13 10:01, Pietro wrote:
I'm working to develop a module that use several machine learning
technique to classify the segments results...
the part concerning the hierarchical segmentation is working quite well...
Out of curiosity: which variables are you planning on using for this
classification ?
FYI, here's a list of variables that colleagues established here as
being the ones they use most in objet-based classification:
- mean band values
- brightness (combination of several band values)
- standard deviation of a certain band within an object
- length/width ratio of an object
- GLCM Homogeneity (Haralick)
All of these are implemented in GRASS, except for the length/width ratio.
And of the Haralick indicator, r.texture is pixel-based, evaluating
texture in a given neighborhood. Don't know how difficult it would be to
add the option to calculate the same indicators within the polygons
defined in a cover map.
Moritz
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