Hi,
Please find my answers enclosed. Bottom line is that we have some
components but we are still missing other ones to perform a complete
OBIA scheme. This thread could be an opportunity to start a discussion
on what is missing. We could then write a Request For Comments about that.
Regards,
Julien
Le 24/01/2017 à 15:46, Sébastien G. a écrit :
Hi,
Since recent OTB versions there is a very nice new framework for
classifictaion (PolygonStatistics, SampleSelection,
SampleExtraction,TrainVectorClassifier) that allow to train model on
vector data.
Can anyone tell me an OTB recipee to use these new tools for
object-based classification, including the following steps :
1- Derive features from a raster images on a segmentation (mean,
standard deviation of radiometric channels, or shape features)
There is this step from the Large Scale MeanShift segmentation workflow :
https://www.orfeo-toolbox.org/CookBook/recipes/improc.html#step-4-vectorization
Will work with any pair of segmentation/input image as long as they
share the same sampling grid (i.e. same size/orgin/spacing).
There are also similar things in this remote module :
https://github.com/sertit/SertitObject
2- Train a vectorClassifier knowing a class attributes on a sample of
the (segmentation with features).
This is covered by TrainVectorClassifier application, which can learn
from points but also from polylines or polygons. The problem is to get
the labels to train the polygons. Aside from manual editing, we are
missing some tools to perform automatic labelling of polygons from a
reference layer.
3- Apply the model on the vector data (segmentation with features)
This is covered by OGRLayerClassifier application;
Best regards,
Sébastien
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CNES - DSO/SI/2A
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