Please use this identifier to cite or link to this item:
Title: Managing uncertainty when aggregating from pixels to objects: habitats, context-sensitive mapping and possibility theory
Authors: Comber, Alexis J.
Medcalf, Katie
Lucas, Richard
Bunting, Peter
Brown, Alan
Clewley, Daniel
Breyer, Johanna
Keyworth, Steve
First Published: 24-Feb-2010
Publisher: Taylor & Francis
Citation: International Journal of Remote Sensing, 2010, 31 (4), pp. 1061-1068
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.
DOI Link: 10.1080/01431160903246691
ISSN: 0143-1161
Type: Article
Rights: This is the author’s final draft of the paper published as International Journal of Remote Sensing, 2010, 31 (4), pp. 1061-1068. The final published version is available at, Doi: 10.1080/01431160903246691.
Appears in Collections:Published Articles, Dept. of Geography

Files in This Item:
File Description SizeFormat 
2. comber_ijrs_2010_pre_pub.pdf1.62 MBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.