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Detecting changed regions between two given synthetic aperture radar images is very important to monitor change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of–the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]