Abstract: Building extraction in urban environment requires high spatial resolution remotely sensed data. However, traditional pixel-based classifiers based on spectral features are ineffective for high-resolution multi-spectral images due to large within-class spectral variations and between-class spectral confusions. In this study, a rule-based object-oriented classification method for building extraction is developed from an Ikonos urban scene. The method includes the following steps: (1) fusion of 1m panchromatic and 4m multispectral bands to produce a pan-sharpened 1m multispectral image; (2) segmentation of the 1m dataset; (3) supervised object-based classification into broad spectral classes; and (4) spectral, spatial, textural and contextual parameters developed from sample building objects are implemented in a fuzzy logic rule base to separate building rooftops from other impervious surface classes. The rule-based method identifies building rooftops with 93% accuracy in the experiment.