The Decision Tree classifier performs multistage classifications by using a series of binary decisions to place pixels into classes. Each decision divides the pixels in a set of images into two classes based on an expression. You can divide each new class into two more classes based on another expression. You can define as many decision nodes as needed. The results of the decisions are classes. You can use data from many different sources and files together to make a single decision tree classifier. You can edit and “prune” the decision trees interactively, and you can save the trees and apply them to other datasets.

See the following topics:

• New Decision Tree
• Execute a New Decision Tree
• Edit an Existing Decision Tree
• Execute an Existing Decision Tree