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Incorporating Knowledge into Unsupervised Model-Based Clustering for Satellite Image

Al_Momani, B, Morrow, PJ and McClean, SI (2007) Incorporating Knowledge into Unsupervised Model-Based Clustering for Satellite Image. In: IEEE/ASC International Conference on Computer Systems and Applications, AICCSA07, Amman-Jordan. IEEE Computer Society. 8 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=4230921&arnumber=4231044&count=151&index=122

DOI: 10.1109/AICCSA.2007.370716


The identification and classification of landcover types from remotely sensed data is traditionally based on the assumption that pixels with similar spatial distribution patterns belong to the same spectral class. However, spectral data on its own has proven to be insufficient for classification. In addition, it is difficult to obtain enough accurate labelled samples from such data. Contextual data can be incorporated or fused' with spectral data to improve the estimation of class labels and therefore enhance the accuracy of the classification process as a whole when labelled data is not available. In this paper we use Dempster-Shafer theory of evidence to fuse the output of an unsupervised model-based clustering (MBC) technique and contextual data in the form of a digital elevation model. The final classification accuracy is shown to improve when using this approach.

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:6879
Deposited By: Professor Philip Morrow
Deposited On:04 May 2010 08:46
Last Modified:09 May 2016 10:52

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