[GRASS-user] Workflow of a classification project with orthophotos

[Nikos Alexandris]

I am still struggling with this. In theory it sounds easy but when it
comes to the point it's quite hard considering that we don't have the
raw data. Any other ideas?

Thank you,
Nikos

On Wed, 2008-07-16 at 16:50 +0200, Nikos Alexandris wrote:
[...]

> My workflow
> 
> 1. Stretch colour orthophotos (8-bit R,G and B bands) from 0 to 255
> values (weither with GDAL or import in GRASS' database and stretch
> inside the DB)
> 
> 2. Visually identify the different "groups" of images taken more or less
> at the same time

This sounds too difficult but we don't have the metadata (i.e. date of
acquisition to reasonably group the tiles based on this information).

> I have some vector of interest areas which correspond to biger
> admnistrative areas (images are from West-Central Germany, groups are
> something like koblenz, trier, simmern and more).
> 
> 3. Split the mosaic in the groups that include photos that present less
> colour differences
> 
> 4. Sampling
> 
> 5. Segmentation with i.smap
> 
> 6. Use r.texture as I think it will boost the accuracy of the
> classification
> 
> 7. Classify
> 
> 8. Some handwork to improve sampling
> 
> 9. Re-Run segmentation, classification
> 
> 10. Handwork to correct obvious errors
> 
> 11. Voila the power of GFOSS ;-)