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.