If a pixel satisfies a certain set of criteria, then the pixel is assigned to the class that corresponds to that criteria. In this process, you select pixels that represent patterns you recognize or can identify with help from other sources. Knowledge of the data, the classes desired, and the algorithm to be used is required before you begin selecting training samples. By setting priorities to these classes, you supervise the classification of pixels as they are assigned to a class value. If the classification is accurate, then each resulting class corresponds to a pattern that you originally identified.
Unsupervised classification is more computer-automated.
It allows you to specify parameters that the computer uses as guidelines to uncover statistical patterns in the data. In this lab you will perform an unsupervised classification in Imagine. In the next lab you will do a supervised classification. This section shows you how to create a thematic raster layer by letting the software identify statistical patterns in the data without using any ground truth data. It begins with either arbitrary cluster means or means of an existing signature set, and each time the clustering repeats, the means of these clusters are shifted.