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.
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. The location of a specific characteristic, such as a land cover type, may be known through ground truthing. Ground truthing refers to the acquisition of knowledge about the study area from field work, analysis or aerial photography, personal experience, etc. They should be collected at the same time as the remotely sensed data, so that the data correspond as much as possible. However, some ground truth data may not be very accurate due to a number of errors, inaccuracies, and human shortcomings.