@@ -72,8 +72,15 @@ classify a set of features on a new vector data file using the
-out classifiedData.shp
This application outputs a vector data file storing sample values
and classification labels. The output is optional, in this case the
input vector data classification label field is updated.
and classification labels. The output vector file is optional. If no output is
given to the application, the input vector data classification label field is
updated. If a statistics file was used to normalize the features during
training, it shall also be used here, during classification.
Note that with this application, the machine learning model may come from a
training on image or vector data, it doesn't matter. The only requirement is
that the chosen features to use should be the same as the one used during
training.
Validating classification
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@@ -535,6 +542,53 @@ and this image is produced with the commands inside this
Figure 2: From left to right: Original image, result image with fusion (with monteverdi viewer) of original image and fancy classification and input image with fancy color classification from labeled image.
Unsupervised learning
---------------------
Using the same machine learning framework, it is also possible to perform
unsupervised classification. In this case, the main difference is that
the training samples don't need a real class label. However, in order to use
the same *TrainImagesClassifier* application, you still need to
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, 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.
::
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 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.
