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Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data

Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised meth...

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Main Authors:	Ahmad, Asmala, Mohd Hashim, Ummi Kalsom, Mohd, Othman, Abdullah, Mohd Mawardy, Sakidin, Hamzah, Rasib, Abd Wahid, Sufahani, Suliadi Firdaus
Format:	Article
Language:	English
Published:	The Science and Information (SAI) Organization Limited 2018
Subjects:	QA75 Electronic computers. Computer science TP200-248 Chemicals: Manufacture, use, etc.
Online Access:	http://eprints.uthm.edu.my/3552/1/AJ%202018%20%28700%29%20Comparative%20analysis%20of%20support%20vector%20machine%2C%20maximum%20likelihood%20and%20neural%20network%20classification%20on%20multispectral%20remote%20sensing%20data.pdf http://eprints.uthm.edu.my/3552/
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spelling	my.uthm.eprints.35522021-11-18T07:07:19Z http://eprints.uthm.edu.my/3552/ Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data Ahmad, Asmala Mohd Hashim, Ummi Kalsom Mohd, Othman Abdullah, Mohd Mawardy Sakidin, Hamzah Rasib, Abd Wahid Sufahani, Suliadi Firdaus QA75 Electronic computers. Computer science TP200-248 Chemicals: Manufacture, use, etc. Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN. The Science and Information (SAI) Organization Limited 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/3552/1/AJ%202018%20%28700%29%20Comparative%20analysis%20of%20support%20vector%20machine%2C%20maximum%20likelihood%20and%20neural%20network%20classification%20on%20multispectral%20remote%20sensing%20data.pdf Ahmad, Asmala and Mohd Hashim, Ummi Kalsom and Mohd, Othman and Abdullah, Mohd Mawardy and Sakidin, Hamzah and Rasib, Abd Wahid and Sufahani, Suliadi Firdaus (2018) Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data. International Journal of Advanced Computer Science and Applications, 9 (9). pp. 529-537. ISSN 2156-5570
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	QA75 Electronic computers. Computer science TP200-248 Chemicals: Manufacture, use, etc.
spellingShingle	QA75 Electronic computers. Computer science TP200-248 Chemicals: Manufacture, use, etc. Ahmad, Asmala Mohd Hashim, Ummi Kalsom Mohd, Othman Abdullah, Mohd Mawardy Sakidin, Hamzah Rasib, Abd Wahid Sufahani, Suliadi Firdaus Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
description	Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.
format	Article
author	Ahmad, Asmala Mohd Hashim, Ummi Kalsom Mohd, Othman Abdullah, Mohd Mawardy Sakidin, Hamzah Rasib, Abd Wahid Sufahani, Suliadi Firdaus
author_facet	Ahmad, Asmala Mohd Hashim, Ummi Kalsom Mohd, Othman Abdullah, Mohd Mawardy Sakidin, Hamzah Rasib, Abd Wahid Sufahani, Suliadi Firdaus
author_sort	Ahmad, Asmala
title	Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
title_short	Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
title_full	Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
title_fullStr	Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
title_full_unstemmed	Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
title_sort	comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
publisher	The Science and Information (SAI) Organization Limited
publishDate	2018
url	http://eprints.uthm.edu.my/3552/1/AJ%202018%20%28700%29%20Comparative%20analysis%20of%20support%20vector%20machine%2C%20maximum%20likelihood%20and%20neural%20network%20classification%20on%20multispectral%20remote%20sensing%20data.pdf http://eprints.uthm.edu.my/3552/
_version_	1738581137552310272
score	13.211869

Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data

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