A target fusion-based approach for classifying high spatial resolution imagery

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. To successfully extract various land covers from high resolution imagery, a Target-Clustering Fusion (TCF) system is presented in this work. 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 neighbors.