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

Alexis Comber, Katie Medcalf, R. M. Lucas, P. Bunting, Alan Brown, D. Clewley, J. Breyer, Steve Keyworth

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)

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.
Original languageEnglish
Pages (from-to)1061-1068
Number of pages8
JournalInternational Journal of Remote Sensing
Volume31
Issue number4
DOIs
Publication statusPublished - 24 Feb 2010

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