Skip to main content

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

Buy Article:

$55.00 plus tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: Department of Geography, University of Leicester, Leicester, UK 2: Environment Systems, 8G Cefn Llan Science Park, Aberystwyth, UK 3: Institute of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK 4: Countryside Council for Wales, Bangor, UK

Publication date: 2010-04-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more