Managing uncertainty when aggregating from pixels to objects : context sensitive mapping and possibility theory

Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and userspecified fuzzy rules. This letter explores the classification and uncertainty issues associated with aggregating detailed ‘sub-objects’ to spatially coarser ‘super-objects’ in object-oriented classifications. We show Possibility Theory to be an appropriate formalism for managing the uncertainty commonly associated with moving from ‘pixels to parcels’ in remote sensing. A worked example demonstrates how Possibility theory and its associated Necessity function, provide measures of certainty and uncertainty and support alternative realisations of the same remotely sensed data that are increasingly required to support different policy objectives.