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

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posted on 2010-06-11, 10:55 authored by Alexis J. Comber, Katie Medcalf, Richard Lucas, Peter Bunting, Alan Brown, Daniel Clewley, Johanna Breyer, Steve Keyworth
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.

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Citation

International Journal of Remote Sensing, 2010, 31 (4), pp. 1061-1068

Published in

International Journal of Remote Sensing

Publisher

Taylor & Francis

issn

0143-1161

Copyright date

2010

Available date

2010-06-11

Publisher version

http://www.tandfonline.com/doi/abs/10.1080/01431160903246691

Language

en

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