Land cover mapping through optimizing remote sensing data for SVM classification
| dc.contributor.advisor | RÏ‹ther, Heinz | en_ZA |
| dc.contributor.author | Gidudu, Anthony | en_ZA |
| dc.date.accessioned | 2014-07-31T11:38:05Z | |
| dc.date.available | 2014-07-31T11:38:05Z | |
| dc.date.issued | 2006 | en_ZA |
| dc.description | Includes bibliographical references (leaves 123-129) | |
| dc.description.abstract | 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. | en_ZA |
| dc.identifier.apacitation | Gidudu, A. (2006). <i>Land cover mapping through optimizing remote sensing data for SVM classification</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,School of Architecture, Planning and Geomatics. Retrieved from http://hdl.handle.net/11427/5599 | en_ZA |
| dc.identifier.chicagocitation | Gidudu, Anthony. <i>"Land cover mapping through optimizing remote sensing data for SVM classification."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,School of Architecture, Planning and Geomatics, 2006. http://hdl.handle.net/11427/5599 | en_ZA |
| dc.identifier.citation | Gidudu, A. 2006. Land cover mapping through optimizing remote sensing data for SVM classification. University of Cape Town. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Gidudu, Anthony AB - 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. DA - 2006 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2006 T1 - Land cover mapping through optimizing remote sensing data for SVM classification TI - Land cover mapping through optimizing remote sensing data for SVM classification UR - http://hdl.handle.net/11427/5599 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/5599 | |
| dc.identifier.vancouvercitation | Gidudu A. Land cover mapping through optimizing remote sensing data for SVM classification. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,School of Architecture, Planning and Geomatics, 2006 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5599 | en_ZA |
| dc.language.iso | eng | en_ZA |
| dc.publisher.department | School of Architecture, Planning and Geomatics | en_ZA |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject.other | Architecture, Planning and Geomatics | en_ZA |
| dc.title | Land cover mapping through optimizing remote sensing data for SVM classification | en_ZA |
| dc.type | Doctoral Thesis | |
| dc.type.qualificationlevel | Doctoral | |
| dc.type.qualificationname | PhD | en_ZA |
| uct.type.filetype | Text | |
| uct.type.filetype | Image | |
| uct.type.publication | Research | en_ZA |
| uct.type.resource | Thesis | en_ZA |