The change vector analysis in posterior probability space (CVAPS) method has been widely used in the change detection of remote sensing images owing to its many advantages. It uses the support vector machine (SVM) to estimate the posterior probability vector. However, in the classification of low and medium resolution remote sensing images, SVM cannot effectively deal with the problems of the same object with the different spectra, different objects with the same spectrum, and mixed pixels and thus cannot guarantee the accuracy of the final detection results. Therefore, this paper adopts the fuzzy c-means (FCM) clustering for modeling and couples the FCM with a simple Bayesian network (SBN) to solve the problem of mixed pixels and estimate the posterior probability vector, thus achieving a new posterior probability space change vector analysis method. The experimental results indicate that, compared to the SVM-based CVAPS algorithm, the algorithm proposed in this study shows higher overall accuracy, higher Kappa coefficient, more reliable performance that is less affected by the number of training samples, simpler parameter setting, and lower time consumption. Therefore, the algorithm proposed in this paper helps to improve the accuracy and efficiency of the change detection of remote sensing images.