Article

Artificial Intelligence Review

, Volume 25, Issue 1, pp 161-178

Using Dempster–Shafer to incorporate knowledge into satellite image classification

  • Bilal Al MomaniAffiliated withSchool of Computing and Information Engineering, Faculty of Engineering, University of Ulster Email author 
  • , Sally McCleanAffiliated withSchool of Computing and Information Engineering, Faculty of Engineering, University of Ulster
  • , Philip MorrowAffiliated withSchool of Computing and Information Engineering, Faculty of Engineering, University of Ulster

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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.

Keywords

Classification Evidential theory Remote sensing Maximum likelihood