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Using Dempster-Shafer to Incorporate Knowledge into Satellite Image Classification

Al_Momani, B, McClean, SI and Morrow, PJ (2007) Using Dempster-Shafer to Incorporate Knowledge into Satellite Image Classification. Artificial Intelligence Review, 25 (1-2). pp. 161-178. [Journal article]

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URL: http://www.springerlink.com/content/r328710171862512/?p=aa957a163aef40379e3f5396503d2062&pi=0

DOI: 10.1007/s10462-007-9027-4


Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster–Shafer’s theory of evidence to achieve this data fusion. Incorporating a Knowledge Base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:6877
Deposited By: Professor Philip Morrow
Deposited On:04 May 2010 08:43
Last Modified:09 May 2016 10:52

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