Fusion of UAVSAR and Quickbird Data for Urban Growth Detection

摘要

Urban areas are rapidly changing all over the world and, therefore, continuous mapping of the changes is essential for urban planners 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. Synthetic aperture radar (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 SAR processing is complementary to that obtained using optical data. This proposed algorithm has been applied on a multi-sensor dataset consisting of optical QuickBird images (RGB) and full polarimetric L-band UAVSAR (Unmanned Aerial Vehicle Synthetic Aperture Radar) image data. After preprocessing the data, the coherency matrix (T), and Pauli decomposition are extracted from multi-temporal UAVSAR images. Next, the SVM (support vector machine) classification method is applied to the multi-temporal features in order to generate two classified maps. In the next step, a post-classification-based algorithm is used to generate the change map. Finally, the results of the change maps are fused by the majority voting algorithm to improve the detection of urban changes. In order to clarify the importance of using both optical and polarimetric images, the majority voting algorithm was also separately applied to change maps of optical and polarimetric images. In order to analyze the accuracy of the change maps, the ground truth change and no-change area that were gathered by visual interpretation of Google earth images were used. After correcting for the noise generated by the post-classification method, the final change map was obtained with an overall accuracy of 89.81% and kappa of 0.8049.