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Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis

Shoreline extraction provides the boundary information of land and water, which helps monitor erosions or accretions of coastal zones. Such monitoring can be performed by using satellite images rather than by using traditional ground survey. To date, shorelines can be extracted from satellite images...

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Main Authors: Abd Manaf, Syaifulnizam, Mustapha, Norwati, Sulaiman, Md. Nasir, Husin, Nor Azura, Mohd Shafri, Helmi Zulhaidi, Abdul Hamid, Mohd Radzi
Format: Article
Language:English
Published: Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka 2017
Online Access:http://psasir.upm.edu.my/id/eprint/64658/1/Quantitative%20validation%20assessment%20on%20shorelines%20extracted%20from%20image%20classification%20techniques%20of%20medium%20resolution%20satellite%20images%20based%20on%20change%20analysis.pdf
http://psasir.upm.edu.my/id/eprint/64658/
http://journal.utem.edu.my/index.php/jtec/article/view/2772
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spelling my.upm.eprints.646582018-08-13T03:45:50Z http://psasir.upm.edu.my/id/eprint/64658/ Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis Abd Manaf, Syaifulnizam Mustapha, Norwati Sulaiman, Md. Nasir Husin, Nor Azura Mohd Shafri, Helmi Zulhaidi Abdul Hamid, Mohd Radzi Shoreline extraction provides the boundary information of land and water, which helps monitor erosions or accretions of coastal zones. Such monitoring can be performed by using satellite images rather than by using traditional ground survey. To date, shorelines can be extracted from satellite images with a high degree of accuracy by using satellite image classification techniques based on machine learning, which helps identify the land and water classes of shorelines. In this study, the results of extracted shorelines of 11 classifiers were validated by using a reference shoreline provided by the local authority. Specifically, the validation assessment was performed using Mean Shoreline Change method to examine the differences between the extracted shorelines and the reference shoreline. The research findings showed that SVM Linear attained the highest number of transects and the lowest mean distances between extracted shorelines and reference shoreline, thus rendering it as the most effective image classification technique in demarcating land and water classes. Furthermore, the findings showed that the accuracy of the extracted shoreline was not directly proportional to the accuracy of the image classification, and smoothing operation using PAEK affected the quality of extracted shorelines. Moreover, the tolerance setting that was ten times the spatial resolution of satellite images was observed to be the most optimal configuration. Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64658/1/Quantitative%20validation%20assessment%20on%20shorelines%20extracted%20from%20image%20classification%20techniques%20of%20medium%20resolution%20satellite%20images%20based%20on%20change%20analysis.pdf Abd Manaf, Syaifulnizam and Mustapha, Norwati and Sulaiman, Md. Nasir and Husin, Nor Azura and Mohd Shafri, Helmi Zulhaidi and Abdul Hamid, Mohd Radzi (2017) Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis. Journal of Telecommunication, Electronic and Computer Engineering, 9 (2-12). pp. 67-73. ISSN 2180-1843; ESSN: 2289-8131 http://journal.utem.edu.my/index.php/jtec/article/view/2772
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 Shoreline extraction provides the boundary information of land and water, which helps monitor erosions or accretions of coastal zones. Such monitoring can be performed by using satellite images rather than by using traditional ground survey. To date, shorelines can be extracted from satellite images with a high degree of accuracy by using satellite image classification techniques based on machine learning, which helps identify the land and water classes of shorelines. In this study, the results of extracted shorelines of 11 classifiers were validated by using a reference shoreline provided by the local authority. Specifically, the validation assessment was performed using Mean Shoreline Change method to examine the differences between the extracted shorelines and the reference shoreline. The research findings showed that SVM Linear attained the highest number of transects and the lowest mean distances between extracted shorelines and reference shoreline, thus rendering it as the most effective image classification technique in demarcating land and water classes. Furthermore, the findings showed that the accuracy of the extracted shoreline was not directly proportional to the accuracy of the image classification, and smoothing operation using PAEK affected the quality of extracted shorelines. Moreover, the tolerance setting that was ten times the spatial resolution of satellite images was observed to be the most optimal configuration.
format Article
author Abd Manaf, Syaifulnizam
Mustapha, Norwati
Sulaiman, Md. Nasir
Husin, Nor Azura
Mohd Shafri, Helmi Zulhaidi
Abdul Hamid, Mohd Radzi
spellingShingle Abd Manaf, Syaifulnizam
Mustapha, Norwati
Sulaiman, Md. Nasir
Husin, Nor Azura
Mohd Shafri, Helmi Zulhaidi
Abdul Hamid, Mohd Radzi
Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
author_facet Abd Manaf, Syaifulnizam
Mustapha, Norwati
Sulaiman, Md. Nasir
Husin, Nor Azura
Mohd Shafri, Helmi Zulhaidi
Abdul Hamid, Mohd Radzi
author_sort Abd Manaf, Syaifulnizam
title Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
title_short Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
title_full Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
title_fullStr Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
title_full_unstemmed Quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
title_sort quantitative validation assessment on shorelines extracted from image classification techniques of medium resolution satellite images based on change analysis
publisher Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/64658/1/Quantitative%20validation%20assessment%20on%20shorelines%20extracted%20from%20image%20classification%20techniques%20of%20medium%20resolution%20satellite%20images%20based%20on%20change%20analysis.pdf
http://psasir.upm.edu.my/id/eprint/64658/
http://journal.utem.edu.my/index.php/jtec/article/view/2772
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score 13.211869

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