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Hi,
When you want to perform the training, the "feat" parameter
should be fed with field names from training_samples.sqlite on
which you want to train.
In your pipeline, you should concatenate your NDVI images into
one stack ( otbcli_ConcatenateImages) then use this stack to
extract the samples values (otbcli_SampleExtraction).
Regards,
Guillaume
On 11/09/2016 11:08 AM, michel M wrote:
hello ,
I have a little question I have 7 images (7 months) Landsat 8
(for a year)
i use this fonction
for f in LAND*.TIF; do otbcli_RadiometricIndices.bat
-channels.blue 2 -channels.green 3 -channels.red 4
-channels.nir 5 -in "$f" -out "NDVI_$f" -list
Vegetation:NDVI; done |
after i do
| ndvi=`for i in $(seq 0 6); do
printf "ndvi_$i "; done` |
and
| otbcli_TrainVectorClassifier.bat
-io.vd training_samples.sqlite -cfield code -classifier
rf -classifier.rf.max 20 -io.out model.rf -feat ${ndvi} |
and i have
2016 Nov 09 11:04:36 : Application.logger (INFO) Precision of
class [28] vs all: 0.0697674
2016 Nov 09 11:04:36 : Application.logger (INFO) Recall of
class [28] vs all: 1
2016 Nov 09 11:04:36 : Application.logger (INFO) F-score of
class [28] vs all: 0.130435
2016 Nov 09 11:04:36 : Application.logger (INFO) Global
performance, Kappa index: 0
there is no vegetation very strange ????
is there the good numbers of channels ?
many thanks
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Guillaume PASERO
Responsable technique
Business Unit E-SPACE & Geo Information -
Département Image & Applications
CS Systèmes d'Information
Parc de la Grande Plaine - 5, Rue Brindejonc des
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31506 Toulouse Cedex 05 - FRANCE
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