Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection

In this paper a new approach based on the fusion of Sentinel-1 and Sentinel-2 products to map urban change detection and to observe suburb's development is presented. The algorithm developed can process data in a fast, automatic and accurate way. To reach this goal, the processing chain uses an iterative multitemporal approach based, for each iteration, on three procedures. The first and second ones are based on Pulse Coupled Neural Network (PCNN) applied to SAR and optical images, respectively, while the third processing is an optical multiband filter, implementing the spectral difference computation. The three outputs of each iteration are fused together by means of a weighted average formulation. The algorithm may deal with multitemporal acquisitions to improve the overall accuracy in the detection of urban changes by the integration of the outputs at different time intervals.