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Fusion of Elevation Data into Satellite Image Classification Using Refined Production Rules

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Al Momani, Bilal, Morrow, Philip and McClean, Sally (2011) Fusion of Elevation Data into Satellite Image Classification Using Refined Production Rules. In: 8th International Conference, ICIAR 2011, Burnaby, BC, Canada. Springer Lecture Notes in Computer Science. Vol 6753 20 pp. [Conference contribution]

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DOI: 10.1007/978-3-642-21593-3_22

Abstract

The image classification process is based on the assumption that pixels which have similar spatial distribution patterns, or statistical characteristics, belong to the same spectral class. In a previous study we have shown how we can improve the accuracy of classification of remotely sensed imagery data by incorporating contextual elevation knowledge in a form of a digital elevation model with the output of the classification process using Dempster-Shafer Theory of Evidence. A knowledge based approach is created for this purpose using suitable production rules derived from the elevation distributions and range of values for the elevation data attached to a particular satellite image. Production rules are the major part of knowledge representation and have the basic form: IF condition THEN Inference. Although the basic form of production rules has shown accuracy improvement, in general, in some cases accuracy can degrade. In this paper we propose a “refined” approach that takes into account the actual “distribution” of elevation values for each class rather than simply the “range” of values to solve the accuracy degradation. This approach is performed by refining the basic production rules used in the previous study taking into account the number of pixels at each elevation within the elevation distribution for each class.

Item Type:Conference contribution (Paper)
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:19061
Deposited By:Professor Sally McClean
Deposited On:15 Jul 2011 11:02
Last Modified:15 Jul 2011 11:02

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