Classification Algorithms

Classification algorithms are generally grouped into supervised and unsupervised
methods, although some algorithms combine features from each group. In the supervised
case, a specialist identifies terrain classes in a scene, and class means and/or
boundaries are identified in parameter space that serve to separate the classes. This is
called "training", and the training data can be chosen from the scene itself, or from
previously acquired scenes that possess similar characteristics. After the training, the
algorithm automatically assigns classes to each pixel based on the predetermined class
means or boundaries.

In a basic unsupervised classifier, the algorithm has no prior information of the
scene content or of the terrain classes present. The algorithm examines the parameter
space for each scene, and assigns classes and boundaries based on the clustering of
pixels. Sometimes, the classes and boundaries can be based upon physical models, e.g. . In either case, the operator must identify each class manually
after the class assignments.

The supervised classifiers have the disadvantage of requiring operator input, and the
classes obtained tend to be scene specific. The unsupervised classifiers sometimes yield
classes whose physical meaning is uncertain. In the next few subsections, an example of
an unsupervised and a supervised classifier are given, which have been applied to
polarimetric radar data. Finally, a promising new classifier is outlined, which combines
the best features of the two previous types.