Since the launch of high-resolution sensors, the use of satellite images as a major source of spatial information has been the subject of extensive research in a broad range of applications. In particular, the extraction of land cover information from remotely sensed data and the use of this information as input into geographical information systems (GIS) has received considerable attention over the last ten years. The successful use of GIS as a decision support tool can only be achieved, if it becomes possible to attach a quality label to the output of each spatial analysis operation. Thus the accuracy of multispectral classification gained more attention. In a GIS, the data is usually stored in terms of objects instead of individual pixels. To this end, the classification result has to be segmented. An important aspect of this research is the propagation of the uncertainty of a pixel belonging to a class to the uncertainty of the pixel belonging to a region. Different approaches for image segmentation will be presented, that take the thematic uncertainty of the pixels into account. They will be applied and verified to a small test area.

Several conceptual models and data models have been proposed for geographical phenomena with uncertainties that cannot easily be forced into current standard data models. These models of uncer-tain objects can be categorized into two groups. One group (Clementini and Di Felice 1996; Cohn and Gotts 1996; Roy and Stell 2001) considers the broad boundary of an object as a homogeneous two-