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 ;-) _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
