Remote sensing data is an important source of information in many applications such as change detection of natural resources, regional, global and local scale, also monitoring of land use and land cover changes and environmental studies. The principle of using satellite images to change detection in multi-temporal satellite images is behavior of spectral change complications during the time. The order of spectral behavior of complications is spectral and texture characteristics of them. Many methods have been developed to change detection which have been made as object-base and pixel-base approaches. The present research focuses on review and study of these methods. In much research, the input data sets have been used for performance evaluation of change detection methods. And these algorithms were compared to each other in terms of speed, simplicity of implementation and change detection accuracy and In most cases The have been conclusions, which these results contradicted the results of other researchers. The accuracy of each method of change detection is a function of the consistency between the algorithms and input data. In fact, accuracy of these methods are not related to the inherent advantage of the algorithm and any algorithm or combination of them absolutely have not advantaged over other methods. In this study, tried that change detection methods are examined from different aspects and be expressed goals of each of them.