In this paper, a comparison between supervised change detection methods for Very High geometrical Resolution satellite images is considered. Methods commonly used for high and medium resolution are here confronted to the problem of exploiting very high resolution imagery, which is characterized by strong redundancy, high variances of information composing objects, collinearity and noise. Three supervised methods for change detection are compared: the Post Classification Comparison, the Direct Multidate Classification and the Difference Image Analysis. Each method is built using Support Vector Machines for the purpose of detecting urban changes between two QuickBird scenes of the city of Zürich, Switzerland. The benefits of adding spatial and contextual information are also studied. Comparison between the performance of the approaches, as well as considerations about the adaptability of such methods to very high geometrical resolution are reported.