I'm new to OTB, but have the same need to do OBIA. I just finished a 
project where we used a proprietary segmentation module then using python 
integrated the results with scikit-learn. I'm rewriting that to work with 
otb LSMS Segmentation which works much better. So my current workflow is 
something like this:

step 1: run LSMSSegmentation and get polygon shapefile with attributes like:

Num     Name            Type    Len     Decimal
1.      LABEL           N       9       0
2.      NBPIXELS        N       9       0
3.      MEANB0          N       24      15
4.      MEANB1          N       24      15
5.      MEANB2          N       24      15
6.      MEANB3          N       24      15
7.      MEANB4          N       24      15
8.      MEANB5          N       24      15
9.      VARB0           N       24      15
10.     VARB1           N       24      15
11.     VARB2           N       24      15
12.     VARB3           N       24      15
13.     VARB4           N       24      15
14.     VARB5           N       24      15


step 2: and then to that I add polygon stats based roughly on FRAGSTATS 
like:

15.     AREA            N       24      15
16.     PERIM           N       24      15
17.     PARA            N       24      15
18.     COMPACT         N       24      15
19.     COMPACT2        N       24      15
20.     SMOOTH          N       24      15
21.     SHAPE           N       24      15
22.     FRAC            N       24      15
23.     CIRCLE          N       24      15

step 3: In my case I load these into a postgres database where we have 
training polygons and we select the polygon segments that match our 
exemplars
step 4: and feed them into scikit-learn to build a trained classifier.
step 5: we segment a search areas and add the polygon stats as above
step 6: we run those segments through trained classifier to get probability 
that they belong to a specific class.

It sounds like otb has modules for the classifiers that I might need to 
look into, but I like using the database just because there is a lot of 
data to manage. Gluing everything together with python means I can automate 
the workflow and the data management of various runs against different 
areas.



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