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Land cover mapping through optimizing remote sensing data for SVM classification
Includes bibliographical references (leaves 123-129)
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
School of Architecture, Planning and Geomatics
2014
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| _version_ | 1867614326305587200 |
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| access_status_str | Open Access |
| author | Gidudu, Anthony |
| author2 | Rϋther, Heinz |
| author_browse | Gidudu, Anthony Rϋther, Heinz |
| author_facet | Rϋther, Heinz Gidudu, Anthony |
| author_sort | Gidudu, Anthony |
| collection | Thesis |
| description | Includes bibliographical references (leaves 123-129) |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/5599 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:50:15.950Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | School of Architecture, Planning and Geomatics |
| publisherStr | School of Architecture, Planning and Geomatics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/5599 Land cover mapping through optimizing remote sensing data for SVM classification Gidudu, Anthony Rϋther, Heinz Architecture, Planning and Geomatics Includes bibliographical references (leaves 123-129) Support Vector Machines (SVMs) are a new supervised classification technique that has its roots in statistical learning theory. It has gained popularity in fields such as machine vision, artificial intelligence, digital image processing and more recently remote sensing. The three commonly used SVMs include linear, polynomial and radial basis function (i.e. Gaussian) classifiers. 2014-07-31T11:38:05Z 2014-07-31T11:38:05Z 2006 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/5599 eng application/pdf application/pdf School of Architecture, Planning and Geomatics Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Architecture, Planning and Geomatics Gidudu, Anthony Land cover mapping through optimizing remote sensing data for SVM classification |
| thesis_degree_str | Doctoral |
| title | Land cover mapping through optimizing remote sensing data for SVM classification |
| title_full | Land cover mapping through optimizing remote sensing data for SVM classification |
| title_fullStr | Land cover mapping through optimizing remote sensing data for SVM classification |
| title_full_unstemmed | Land cover mapping through optimizing remote sensing data for SVM classification |
| title_short | Land cover mapping through optimizing remote sensing data for SVM classification |
| title_sort | land cover mapping through optimizing remote sensing data for svm classification |
| topic | Architecture, Planning and Geomatics |
| url | http://hdl.handle.net/11427/5599 |
| work_keys_str_mv | AT giduduanthony landcovermappingthroughoptimizingremotesensingdataforsvmclassification |