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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Science and Information Organization
2018
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| Online Access: | http://eprints.utm.my/84587/1/AbdWahidRasib2018_ComparativeAnalysisofSupportVectorMachine.pdf |
| _version_ | 1796863876933877760 |
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| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T20:33:17Z |
| format | Article |
| id | utm.eprints-84587 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:33:17Z |
| publishDate | 2018 |
| publisher | Science and Information Organization |
| record_format | dspace |
| spelling | utm.eprints-845872020-02-27T03:05:53Z http://eprints.utm.my/84587/ 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 NA Architecture QA75 Electronic computers. Computer science 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. Science and Information Organization 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/84587/1/AbdWahidRasib2018_ComparativeAnalysisofSupportVectorMachine.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 2158-107X http://dx.doi.org/10.14569/IJACSA.2018.090966 DOI:10.14569/IJACSA.2018.090966 |
| spellingShingle | NA Architecture QA75 Electronic computers. Computer science 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 |
| title | 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_short | 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 |
| topic | NA Architecture QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/84587/1/AbdWahidRasib2018_ComparativeAnalysisofSupportVectorMachine.pdf |
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