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Proceedings Paper

Comparison of supervised classification methods applied on high-resolution satellite images
Author(s): Ayse Ozturk
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Paper Abstract

High resolution satellite images contain rich structural and spatial detail. The availability of high-resolution satellite images provides easy and cost-effective mapping of land features that was not possible using medium or low resolution imagery. Since supervised classification methods give better results for accuracy and performance, supervised classification methods are preferred. By increasing separability of several land-use types, it is possible to group a satellite image into subparts which lead to solution to land cover mapping problem with application of supervised classification methods. Supervised classification methods use prior examples as a training to classify other unseen samples. In this study, a 'traditional' supervised classification method called Maximum Likelihood Classifier (MLC) and Support Vector Machines (SVM) are compared. No single classifier is proven yet to satisfactorily classify all the basic land cover classes that mean there is no best classifier yet for both performance and accuracy. However, individual evaluations together with pros and cons of each method could give insight about applications of the methods compatible with the intent. MLC is statistical parametric method based on probability calculations, but small dataset size causes some problems. SVMs as non-parametric method have longer training time with comparable or better accuracy. Thus, SVM is a good candidate for satellite imagery classification works although its application on satellite image is a pretty new topic. By applying different penalty value (c) and gamma (γ) parameters in the SVM algorithm, changes in the classification results could be observed.

Paper Details

Date Published: 27 October 2011
PDF: 6 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81801D (27 October 2011); doi: 10.1117/12.898332
Show Author Affiliations
Ayse Ozturk, Yalova Univ. (Turkey)


Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

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