An SVM Multiclassifier Approach to Land Cover Mapping

Simple item page
dc.contributor.authorGidudu, Anthony
dc.contributor.authorGregg, Hulley
dc.contributor.authorTshilidzi, Marwala
dc.date.accessioned2023-01-31T19:35:46Z
dc.date.available2023-01-31T19:35:46Z
dc.date.issued2020
dc.description.abstractFrom the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a ‘committee’ of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian (Radial Basis Function) SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.en_US
dc.identifier.citationAnthony, G., Gregg, H., & Tshilidzi, M. (2010). An SVM multiclassifier approach to land cover mapping. arXiv preprint arXiv:1007.1766.en_US
dc.identifier.urihttps://arxiv.org/abs/1007.1766
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7439
dc.language.isoenen_US
dc.publisherASPRSen_US
dc.subjectEnsemble Systemsen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectLand Cover Mappingen_US
dc.titleAn SVM Multiclassifier Approach to Land Cover Mappingen_US
dc.typeOtheren_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AN SVM MULTICLASSIFIER APPROACH TO LAND COVER MAPPING.pdf
Size:
729.38 KB
Format:
Adobe Portable Document Format
Description:
Conference Paper
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Engineering and Technology