Managing uncertainty when aggregating from pixels to objects: habitats, context-sensitive mapping and possibility theory
Year: 2010
Type: article
Abstract: Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and user-specified fuzzy rules. This paper 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 with habitats demonstrates how possibility theory and its associated necessity function provide measures of certainty and uncertainty and support alternative realizations of the same remotely sensed data that are increasingly required to support different applications.
Authors: Alexis Comber, Katie Medcalf, Richard Lucas, Peter Bunting, Alan Brown, Daniel Clewley, Johanna Breyer, Steve Keyworth
Institutions: University of Leicester, Aberystwyth University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2034914914