Abstract: A novel change detection approach based on the dynamic fuzzy Fisher classifier for multitemporal remote sensing images is proposed in this paper, which detects the changes with the joint histogram. To increase the separability of the unlabeled pixels, a non-local mean weighted method is used to introduce the spatial information. The unlabeled pixels are labeled with a predefined probability based on their predictive values. The weights of the unlabeled pixels and the parameters of the dynamic classifier are adjusted according to the updated samples until all the pixels are classified. The proposed method is distribution free, context-sensitive and not affected by the comparison operators. Experimental results on real multitemporal remote sensing images confirm the effectiveness of the proposed technique.