The extraction of urban patterns from Very High Spatial Resolution (VHSR) images presents several challenges related to the size, the accuracy and the complexity of the considered data. In order to assist the end-user to efficiently carry out this task, a new approach is proposed for hierarchically extracting segments of interest from lower resolution data and finally determining urban patterns in these segments from VHSR ones. Based on an intuitive modus operandi, it allows the end-user to progressively segment images based on low level –spatial and radiometric– criteria. Experiments performed on urban images datasets provide encouraging results which may be further used for objects detection and classification purpose.