Can anybody tell me why the classification I so carefully preserve on v.to.rast from my training data does not reflect on my classification?
When I query my vector training data I get:
v.what --v -a map=traintest210912a@3420 east_north=-80606.881455,-3785043.808541 distance=388.789822
East: -80606.881455
North: -3785043.808541
Map: traintest210912a
Mapset: 3420
Type: Area
Sq Meters: 6133078.387
Hectares: 613.308
Acres: 1515.517
Sq Miles: 2.3680
Layer: 1
Category: 1
Driver: dbf
Database: /home/tms/LC_NGIqxx/LO21/3420/dbf/
Table: traintest210912a
Key column: cat
cat : 1
cat_ : 12
label : shrubs and bushes
rgb : 38: 181: 113
Building spatial index...
(Thu Sep 27 14:29:00 2012) Command finished (0 sec)
When I convert this to raster ensuring ‘cat_’ remains the key column and I query the resulting raster I get:
r.what --v -f input=trainteset1_rast@3420 east_north=-80568.002473,-3785121.566505
-80568.002473|-3785121.566505||12|
(Thu Sep 27 14:30:58 2012) Command finished (0 sec)
And when I query the classification after running i.gensig and i.maxlik, I get:
r.what --v -f input=sclasstest1@3420 east_north=-80412.486544,-3783994.076020
-80412.486544|-3783994.076020||1|
(Thu Sep 27 14:31:57 2012) Command finished (0 sec)
Which means I am now forced to repeat myself and run recode or reclass, after going to the hassle of setting the classification codes for a supervised classificaton in the begining.
It would make sense to me if I had run i.class (as per the older documentation I have found that doesn’t include i.gensig), but not with a training map.
Is there maybe a background setting I haven’t found yet that I need to set? Or is this how it is supposed to run? In which case, is there a technical reason for this that I am missing?
Thank you
Sam