Commit 5a10a36a authored by Guillaume Pasero's avatar Guillaume Pasero

DOC: CookBook improvements on unsupervised classification

parent 8ac6bf6d
......@@ -553,16 +553,42 @@ provide a vector data file with a label field. This vector file will be
used to extract samples for the training. Each label value is can be considered
as a source area for samples, the same logic as in supervised learning is
applied for the computation of extracted samples per area. Hence, for
unsupervised classification :
unsupervised classification, the samples are selected based on classes that are
not actually used during the training. For the moment, only the KMeans
algorithm is proposed in this framework.
- if there is a unique label in the vector data, samples will be selected as if
they come from a single class or set
::
otbcli_TrainImageClassifier
-io.il image.tif
-io.vd training_areas.shp
-io.out model.txt
-sample.vfn Class
-classifier sharkkm
-classifier.sharkkm.k 4
- if multiple labels are present, samples will be selected so that every
samples in the smallest class are selected, and the same number of sample from
each class
If your training samples are in a vector data file, you can use the application
*TrainVectorClassifier*. In this case, you don't need a fake label field. You
just need to specify which fields shall be used to do the training.
::
otbcli_TrainVectorClassifier
-io.vd training_samples.shp
-io.out model.txt
-feat perimeter area width red nir
-classifier sharkkm
-classifier.sharkkm.k 4
Once you have the model file, the actual classification step is the same as
the supervised case. The model will predict labels on your input data.
::
otbcli_ImageClassifier
-in input_image.tif
-model model.txt
-out kmeans_labels.tif
Fusion of classification maps
-----------------------------
......
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