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

[Nikos Alexandris]

Dear GRASSers,

I would like to have a confirmation that my feet are on the ground when
I try to realise the following work-flow with G-FOSS. I want to classify
forest gaps out of orthophotos (...actually it's not my job but I want
to help somebody who intented to do all by hand or accept what "others"
say that this task cannot be done unless one utilises commercial
tools... !)

I have more than 300 tiles of a mosaic composed by on aerial imagery.
Unfortunately it is a mixture of different acquisitions (date) and has
significant contrast differences in some regions.

My class-scheme would be gaps, shadows of tree-stands withing the gaps
water, vegetation, urban surfaces.

I can not perform any normalisation the way I know it for some number of
pictures (e.g. for 3,4 satelliteimagery). First of all there are no
overlapping areas and I am not aware (practically) of any other method
to perform a colour balance. 

Anyone struggling with normalisation, colour balancing issues without
having the meta-data (date of acquisition) nor the raw data?

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

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 ;-)


Cheers,
Nikos