ABSTRACT
Recent research on remote sensing classification has focused on modelling and analysis of classification uncertainty. Both fuzzy and probabilistic approaches have been applied (Foody, 1996; Hootsmans, 1996; Canters, 1997; Fisher, 1999; Zhang and Foody, 2001). Much of this research, however, focused on uncertainty of spectral classification on a pixel-by-pixel basis, ignoring potentially useful spatial information
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between pixels. An object-based approach instead of a pixel-based approach may be helpful in reducing classification uncertainty. Additionally, interpretation of uncertainty of real world objects may be more intuitive than interpretation of uncertainty of individual pixels. In this study, uncertainty arises from vagueness, which is characteristic for those geographical objects that are difficult to define, both thematically and in their spatial extent.
