Using Dempster---Shafer to incorporate knowledge into satellite image classification

Authors:
Bilal Al Momani;Sally Mcclean;Philip Morrow
Affiliations:
School of Computing and Information Engineering, Faculty of Engineering, University of Ulster, Coleraine, Northern Ireland, UK BT52 1SA;School of Computing and Information Engineering, Faculty of Engineering, University of Ulster, Coleraine, Northern Ireland, UK BT52 1SA;School of Computing and Information Engineering, Faculty of Engineering, University of Ulster, Coleraine, Northern Ireland, UK BT52 1SA
Venue:
Artificial Intelligence Review
Year:
2006

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Abstract

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.