Urban areas are rapidly changing all over the world and therefore, the continuous mapping of the changes are essential for the urban planner and decision makers. Urban changes can be mapped and measured by using remote sensing data and techniques along with several statistical measures. The urban scene is characterized by very high complexity, containing objects formed from different types of man-made materials as well as natural objects. The aim of this study is to detect urban growth, which can be further utilized for urban planning. Although high-resolution optical data can be used to determine classes more precisely, it is still difficult to distinguish classes such as residential regions with different building type due to spectral similarities. SAR data provide valuable information about the type of scattering backscatter from an object in the scene as well as its geometry and its dielectric properties. Therefore, the information obtained using the SAR processing is complementary to that obtained using optical data. This proposed algorithm has been applied to multi-sensor dataset consisting of the optical QuickBird (RGB) image and full polarimetric L-band UAVSAR image data. After preprocessing data, the coherency matrix (T), and Pauli decomposition are extracted from multi-temporal UAVSAR images. Next, SVM (Support vector machines) classification method is applied to the multi-temporal features in order to generate two classified maps. In the next step, post classification based algorithm was used for generating the change map. Finally, the results of the change maps are fused by the majority voting algorithm to improve the detecting of the urban changes. In order to clarify the importance of using both optical and polarimetric images, the majority voting algorithm was also applied to change maps of optical and polarimetric images separately. In order to analyze the accuracy of the change maps, the ground truth change and no-change area that gathered by visual interpretation of Google earth images were used. After correcting the noise generated by the post-classification method, the final change map was obtained with an overall accuracy of 89.81% and Kappa of 0.80.