Multispectral classification is the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values. If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to that criteria. An example of a classified image is a land cover map, showing vegetation, bare land, pasture, urban, etc. In this short course you will learn satellite images classification definition, its importance, and its types. Also, you will learn with more focus unsupervised classification and its related topics. During the course you will perform various unsupervised classification processes on a satellite image. After classifying a satellite image to a group of related classes, you will learn how to rename each class with the name of its real feature, and recolor it with suitable color, and finally, how to record all data associated with each class in the attribute table. The Classification process is essential in producing different types of maps, such as [geological, agricultural, soil, and others].