@@ -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 +542,27 @@ 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 :
- if there is a unique label in the vector data, samples will be selected as if
they come from a single class or set
- 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
