Change detection determines changes in multitemporal images, which are widely used in deforestation, land use, and urban expansion, among others. Nonetheless, traditional pixel-based change detection methods cause confusion when used on high-spatial images, and generat salt-and-pepper noises on the changed map because of the presence of heterogeneous objects at a pixel level. The useful spatial or contextual information regarding the values of proximate pixels is typically ignored in a pixel-based method; therefore, an object-based method is a new approach to solve these problems in high-spatial resolution images. This study proposes a multilevel object-based method to detect changes in such images. First, we utilize the mean-shift segmentation method to segment the image and consider the heterogeneity of ground objects. Geographic objects are acquired from the segment results at different levels through multilevel information. Then, we combine the gray data of each geographic object at different scale levels to build a feature vector. The change vector analysis method is used to construct an intensity difference map at the multitemporal phase. The change results are generated by applying the expectation maximization algorithm to automatically obtain the thresholds for changed and unchanged areas; moreover, the multiscale change detection algorithm is verified with QuickBird images of urban and rural areas. The pixel-based, single scale level object-based, and multiscale level object-based methods are also compared with one another based on these two datasets. Results show that the change detection accuracy of the object-based change detection method is higher than that of the pixel-based approach in both urban and rural areas. When the total scale level number considered in the change detection method is fixed, the multiscale, object-based method always performs better than the single level object-based method. The change detection accuracies in the urban and rural areas are 87.57% and 81.55%, respectively. Furthermore, the qualitative analysis findings related to the change-detection maps suggest that the proposed technique induces high fidelity in both homogenous and small or border regions. Thus, the proposed algorithm can satisfy the requirements of change detection in urban and rural areas, which benefits land and resource monitoring.