Land cover mapping through optimizing remote sensing data for SVM classification

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.

Grantor dc:publisher.institution

School of Architecture, Planning and Geomatics

Year dc:date.issued

2006

Author dc:creator

• Gidudu, Anthony

Advisor dc:contributor.advisor

• Rϋther, Heinz

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/5599

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/5599