Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia
Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. Such an activity entails ground survey, topographic survey, aerial photo, or remote sensing techniques to extract the shoreline. For example, the remote sensing technique to determine shorelines involv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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American Scientific Publishers
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/64657/1/Artificial%20neural%20networks%20for%20satellite%20image%20classification%20of%20shoreline%20extraction%20for%20land%20and%20water%20classes%20of%20the%20north%20west%20coast%20of%20Peninsular%20Malaysia.pdf |
| _version_ | 1796977941382430720 |
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| author | Abd Manaf, Syaifulnizam Mustapha, Norwati Sulaiman, Md. Nasir Husin, Nor Azura Abdul Hamid, Mohd Radzi |
| author_facet | Abd Manaf, Syaifulnizam Mustapha, Norwati Sulaiman, Md. Nasir Husin, Nor Azura Abdul Hamid, Mohd Radzi |
| author_sort | Abd Manaf, Syaifulnizam |
| collection | UPM |
| description | Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. Such an activity entails ground survey, topographic survey, aerial photo, or remote sensing techniques to extract the shoreline. For example, the remote sensing technique to determine shorelines involves the extraction of relevant data from satellite images. Specifically, the satellite image classification enables shorelines to be extracted from land and water classes with a high degree of precision. However, extracting information from satellite images is challenging as it relies on a strong understanding of image processing, machine learning, and data mining techniques. Thus, the researchers discuss the study of the pixel-based classification of machine learning techniques to classify land and water classes in terms of accuracy, training time, and testing time. The research findings showed that the Multilayer Perceptron Artificial Neural Network (MLP ANN) was the most effective technique, compared with other techniques, hence reinforcing its importance in classifying land and water classes. |
| first_indexed | 2024-03-06T09:47:25Z |
| format | Article |
| id | upm.eprints-64657 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:47:25Z |
| publishDate | 2018 |
| publisher | American Scientific Publishers |
| record_format | dspace |
| spelling | upm.eprints-646572018-08-13T03:45:52Z http://psasir.upm.edu.my/id/eprint/64657/ Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia Abd Manaf, Syaifulnizam Mustapha, Norwati Sulaiman, Md. Nasir Husin, Nor Azura Abdul Hamid, Mohd Radzi Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. Such an activity entails ground survey, topographic survey, aerial photo, or remote sensing techniques to extract the shoreline. For example, the remote sensing technique to determine shorelines involves the extraction of relevant data from satellite images. Specifically, the satellite image classification enables shorelines to be extracted from land and water classes with a high degree of precision. However, extracting information from satellite images is challenging as it relies on a strong understanding of image processing, machine learning, and data mining techniques. Thus, the researchers discuss the study of the pixel-based classification of machine learning techniques to classify land and water classes in terms of accuracy, training time, and testing time. The research findings showed that the Multilayer Perceptron Artificial Neural Network (MLP ANN) was the most effective technique, compared with other techniques, hence reinforcing its importance in classifying land and water classes. American Scientific Publishers 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64657/1/Artificial%20neural%20networks%20for%20satellite%20image%20classification%20of%20shoreline%20extraction%20for%20land%20and%20water%20classes%20of%20the%20north%20west%20coast%20of%20Peninsular%20Malaysia.pdf Abd Manaf, Syaifulnizam and Mustapha, Norwati and Sulaiman, Md. Nasir and Husin, Nor Azura and Abdul Hamid, Mohd Radzi (2018) Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia. Advanced Science Letters, 24 (2). pp. 1382-1387. ISSN 1936-6612; ESSN: 1936-7317 https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000002/art00128 10.1166/asl.2018.10754 |
| spellingShingle | Abd Manaf, Syaifulnizam Mustapha, Norwati Sulaiman, Md. Nasir Husin, Nor Azura Abdul Hamid, Mohd Radzi Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia |
| title | Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia |
| title_full | Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia |
| title_fullStr | Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia |
| title_full_unstemmed | Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia |
| title_short | Artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of Peninsular Malaysia |
| title_sort | artificial neural networks for satellite image classification of shoreline extraction for land and water classes of the north west coast of peninsular malaysia |
| url | http://psasir.upm.edu.my/id/eprint/64657/1/Artificial%20neural%20networks%20for%20satellite%20image%20classification%20of%20shoreline%20extraction%20for%20land%20and%20water%20classes%20of%20the%20north%20west%20coast%20of%20Peninsular%20Malaysia.pdf |
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