Image change detection is to recognize the changes between two images which are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised change detection. However, primary fuzzy clustering is easy to be trapped into a local optimum due to the limits of its optimization process. To tackle the problem, a novel differential evolution algorithm with automatically learning selection strategy is proposed in this paper. Different from the selection rules of classical differential evolution algorithm, this method firstly labels all original individuals and trial individuals according to the scope of the individual fitness at each generation, which will preliminarily determine whether they are selected for the next generation. Secondly, in order to increase the diversity of the population, we choose a few individuals from the nonselected population with a low probability into selected ones before. Finally, the samples including partial individuals from the selected and non- selected lists are used to train the neural networks that will learn the selection strategy. This method will learn different selection strategy in every generation, which will significantly accelerate the convergence speed. The proposed change detection method, combining fuzzy clustering with newly designed differential evolution algorithm, show excellent performance. Experiments conducted on Synthetic Aperture Radar images have demonstrated the superiority of the proposed method.
