Citation
Mohd Shafri, Helmi Zulhaidi and Ramle, F. S. H.
(2009)
A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island.
Information Technology Journal, 8 (1).
pp. 64-70.
ISSN 1812-5638; ESSN: 1812-5646
Abstract
This study investigates a new approach in image classification. Two classifiers were used to classify SPOT 5 satellite image; Decision Tree (DT) and Support Vector Machine (SVM). The Decision Tree rules were developed manually based on Normalized Difference Vegetation Index (NDVI) and Brightness Value (BV) variables. The classification using SVM method was implemented automatically by using four kernel types; linear, polynomial, radial basis function and sigmoid. The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. The overall accuracy of the SVM using four kernel types was above 73% and the overall accuracy of the DT method was 69%.
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Official URL or Download Paper: http://scialert.net/abstract/?doi=itj.2009.64.70
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Additional Metadata
| Item Type: | Article |
|---|---|
| Subject: | Remote-sensing - Malaysia |
| Subject: | Decision trees - Malaysia |
| Subject: | Plant diversity - Malaysia |
| Divisions: | Faculty of Engineering |
| DOI Number: | https://doi.org/10.3923/itj.2009.64.70 |
| Publisher: | Asian Network for Scientific Information |
| Keywords: | Classifiers; Remote sensing; Artificial intelligence; Biodiversity |
| Depositing User: | Anas Yahaya |
| Date Deposited: | 08 Aug 2011 07:42 |
| Last Modified: | 23 Oct 2015 02:21 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3923/itj.2009.64.70 |
| URI: | http://psasir.upm.edu.my/id/eprint/18007 |
| Statistic Details: | View Download Statistic |
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