Land cover mapping through optimizing remote sensing data for SVM classification
Doctoral Thesis
2006
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University of Cape Town
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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.
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Includes bibliographical references (leaves 123-129)
Reference:
Gidudu, A. 2006. Land cover mapping through optimizing remote sensing data for SVM classification. University of Cape Town.