Data extraction from satellite images has significantly developed in recent decades, and various algorithms have been introduced to extract information from satellite imagery, each of which has advantages and disadvantages.
 In general, the methods of classification of satellite images fall into two types of supervised and unsupervised classifications. Furthermore, supervised classification methods are divided into two parametric and non-parametric methods. In this paper, the purpose is to introduce and study the algorithms of parametric supervised and unsupervised classifications of satellite images in terms of accuracy and method of extracting information. Finally, by comparing the algorithms of the existing methods, we conclude that the method of maximal similarity is more accurate than the minimum distance and parallelepiped methods, but it is still not possible to achieve the desirable precision in classification using this method. In fact, statistical methods such as maximal similarity can not be used if the goal is acquiring high precision, because the method of maximal similarity is a completely statistical method, and therefore, it can not provide the ability to accept and use external information in a non-statistical form such as geometry of imaging, geometry of features whose images are being taken, as well as effective factors such as the atmosphere in the classification process, and this is one of the weaknesses of maximal similarity classification in comparison to model-based methods. Therefore, methods such as model-based and science-based have been introduced to improve this method and eliminate its problems.