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To extract GIS features from high spatial resolution imagery is an important task in remote sensing applications. However, traditional pixel-based classification methods, which were developed in the era of 10-100 m ground pixel size imagery, cannot exploit the advantages of new images provided by IKONOS and QuickBird. This is due to the increase of the within-class variability inherent from more detailed and higher spatial resolution data. To successfully extract various land covers from high resolution imagery, a target-clustering fusion (TCF) system is presented in the paper. Compared to the conventional classification methods that typically produce more salt-and-pepper-like results, the proposed TCF system can preserve detailed spatial information on each classified target related to its neighbours. To evaluate the efficacy of TCF, experiments are conducted using real IKONOS images.