表紙画像

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...

詳細記述

保存先:
書誌詳細
主要な著者: Mohd Shafri, Helmi Zulhaidi, Ramle, F. S. H.
フォーマット: 論文
言語:English
出版事項: Asian Network for Scientific Information 2009
オンライン･アクセス: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
http://psasir.upm.edu.my/id/eprint/18007/
http://scialert.net/abstract/?doi=itj.2009.64.70
タグ: タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
id my.upm.eprints.18007
record_format eprints
spelling my.upm.eprints.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 10.3923/itj.2009.64.70
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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%.
format Article
author Mohd Shafri, Helmi Zulhaidi
Ramle, F. S. H.
spellingShingle Mohd Shafri, Helmi Zulhaidi
Ramle, F. S. H.
A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island
author_facet Mohd Shafri, Helmi Zulhaidi
Ramle, F. S. H.
author_sort Mohd Shafri, Helmi Zulhaidi
title 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_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_sort comparison of support vector machine and decision tree classifications using satellite data of langkawi island
publisher Asian Network for Scientific Information
publishDate 2009
url 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
http://psasir.upm.edu.my/id/eprint/18007/
http://scialert.net/abstract/?doi=itj.2009.64.70
_version_ 1643826688396623872
score 13.250246

類似資料