A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island
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 Val...
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| Format: | Article |
| Language: | English |
| Published: |
Asian Network for Scientific Information
2009
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| Online Access: | http://psasir.upm.edu.my/id/eprint/18007/ http://psasir.upm.edu.my/id/eprint/18007/1/A%20comparison%20of%20support%20vector%20machine%20and%20decision%20tree%20classifications%20using%20satellite%20data%20of%20Langkawi%20Island.pdf |
| _version_ | 1848843400297578496 |
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| author | Mohd Shafri, Helmi Zulhaidi Ramle, F. S. H. |
| author_facet | Mohd Shafri, Helmi Zulhaidi Ramle, F. S. H. |
| author_sort | Mohd Shafri, Helmi Zulhaidi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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%. |
| first_indexed | 2025-11-15T08:14:25Z |
| format | Article |
| id | upm-18007 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:14:25Z |
| publishDate | 2009 |
| publisher | Asian Network for Scientific Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-180072015-10-23T02:21:22Z http://psasir.upm.edu.my/id/eprint/18007/ A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island Mohd Shafri, Helmi Zulhaidi Ramle, F. S. H. 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%. Asian Network for Scientific Information 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/18007/1/A%20comparison%20of%20support%20vector%20machine%20and%20decision%20tree%20classifications%20using%20satellite%20data%20of%20Langkawi%20Island.pdf 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 http://scialert.net/abstract/?doi=itj.2009.64.70 Remote-sensing - Malaysia Decision trees - Malaysia Plant diversity - Malaysia 10.3923/itj.2009.64.70 |
| spellingShingle | Remote-sensing - Malaysia Decision trees - Malaysia Plant diversity - Malaysia Mohd Shafri, Helmi Zulhaidi Ramle, F. S. H. A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island |
| title | A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island |
| title_full | A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island |
| title_fullStr | A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island |
| title_full_unstemmed | A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island |
| title_short | A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island |
| title_sort | comparison of support vector machine and decision tree classifications using satellite data of langkawi island |
| topic | Remote-sensing - Malaysia Decision trees - Malaysia Plant diversity - Malaysia |
| url | http://psasir.upm.edu.my/id/eprint/18007/ http://psasir.upm.edu.my/id/eprint/18007/ http://psasir.upm.edu.my/id/eprint/18007/ http://psasir.upm.edu.my/id/eprint/18007/1/A%20comparison%20of%20support%20vector%20machine%20and%20decision%20tree%20classifications%20using%20satellite%20data%20of%20Langkawi%20Island.pdf |