In order to overcome errors caused by season, shooting angle, and other factors in multitemporal remote sensing image change detection, a method based on old-temporal vector data and new-temporal image data is proposed under the premise that changes of images are normally less than the unchanged ones. Getting the object through incremental segmentation under the constraints of the previous vector data, we extract its textural and spectral features to get the dataset by the transform of principal component analysis. After this, the isolation forest method is used to calculate the object’s change index, and the change threshold is obtained by the Bayes method. We conduct two experiments. The effectiveness of the proposed method was verified by comparing image–image and vector–image change detection methods as well as Mahalanobis distance and isolation forest change methods for which the accuracy rate of experiment 1 is 92.35% and that of experiment 2 is 93.18%.