Since little prior knowledge about remote sensing images can be obtained before performing recognition tasks, various unsupervised classification methods have been applied to solve such problem. Therefore, choosing an appropriate clustering method is very critical to achieve good results. However, there is no standard criterion on which clustering method is more suitable or more effective. In this paper, we conduct a comparative study on three clustering methods, including C-Means, Finite Mixture Model clustering, and Affinity Propagation. The advantages and disadvantages of each method are evaluated by experiments and classification results.