Fusion of very high spatial resolution multispectral (VHR) images and lower spatial resolution image time series with more spectral bands can improve land cover classification, combining geometric and semantic advantages of both sources. This study presents a workflow to extract the extent of urban areas using decision-level fusion of individual classifications on Sentinel2 (S2) and SPOT6 satellite images. First, both sources are classified individually in five classes, using state-of-the-art supervised classification approaches and Convolutional Neural Networks. Obtained results are merged in order to extract buildings as accurately as possible. Then, detected buildings are merged again with the S2 classification to extract urban area; a prior to be in an urban area is derived from these building objects and merged with a binary classification derived from the original S2 classification. Both fusions involve a per pixel decision level fusion followed by a contrast sensitive regularization.