The last few decades witnessed high urban growth rates in many countries. Urban growth can be mapped and measured by using remote sensing data and techniques along with several statistical measures. The purpose of this research is to detect the urban change that is used for urban planning. Change detection using remote sensing images can be classified into three methods: algebra-based, transformation-based and classification-based. By using any of these methods and applying them to SAR and optical data has advantages and disadvantages. Fusion of these methods and datasets can give us this opportunity to overcome their disadvantages and complement each other. For this purpose, here, a decision level fusion technique based on the majority voting algorithm is proposed for integrating the change maps extracted by different methods. After extracting features for optical and polarimetric data, object-based and pixel-based classification methods applied to optic images and also Wishart and SVM classification methods applied on SAR data. Change maps extracted from applying different CD methods such as post-classification, image differencing and principal component analysis. In order to evaluate the efficiency of the proposed method, various optical and radar remote sensing images from before and after of urban growth, acquired by QuickBird and UAVSAR, were utilized. In order to clarify the importance of using both optical and polarimetric images, the majority voting fusion algorithm on the change maps extracted by optical and polarimetric images was also applied separately. The results show that by fusing optical and polarimetric data at the decision level, it is possible to obtain a better accuracy because these two types of data, due to differences, can detect changes in a different way, thus covering each other's deficiencies. Polarimetric images better detect changes in altitude changes, and optical images better detect changes resulting from spectral changes. In order to perform a comparative evaluation, the accuracy of the change map obtained using optical images (total accuracy: 80.86% and kappa: 0.67), polarimetric images (overall accuracy: 75.43% and kappa: 0.5), simultaneous applying both datasets (overall accuracy: 88.48% and kappa: 0.79), as well as using the change maps of both data sets with the highest accuracy (overall accuracy: 88.81% and kappa: 0.79) have been obtained. In the end, due to the noise characterization of the post-classification method, the obtained change map improves with an overall accuracy of 90.11% and a kappa of 0.82.