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Knowledge-based semi-supervised satellite image classification

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 (2007) Knowledge-based semi-supervised satellite image classification. In: 9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007) . IEEE Computer Society. 4 pp. [Conference contribution]

Full text not available from this repository.

URL: http://dx.doi.org/10.1109/ISSPA.2007.4555340

DOI: doi:10.1109/ISSPA.2007.4555340


Spectral information on its own has proven to be insufficient for classification of remotely sensed images. In general, it is difficult to distinguish between types of land-cover classes that have similar or identical spectral signatures from remotely sensed data. Contextual data can be dasiafusedpsila with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer theory of evidence to fuse the output of a semi-supervised classification (SSC) technique with 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:13571
Deposited By:Professor Philip Morrow
Deposited On:04 May 2010 09:41
Last Modified:04 May 2010 09:41

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