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Supervised classification and improved filtering method for shoreline detection

Shoreline monitoring is important to overcome the problems in the measurement of the shoreline. Recently, many researchers have directed attention to methods of predicting shoreline changes by the use of multispectral images. However, the images being captured tend to have several problems due to th...

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Main Authors: Zulkifle, F. A., Hassan, R., Othman, R. M., Sallow, A. B.
Format: Article
Language:English
Published: Asian Research Publishing Network 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/76645/1/RohayantiHassan2017_SupervisedClassificationandImprovedFiltering.pdf
http://eprints.utm.my/id/eprint/76645/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032821943&partnerID=40&md5=552fb86048646216e07b4392dcd221b2
http://www.jatit.org/volumes/Vol95No20/31Vol95No20.pdf
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spelling my.utm.766452018-04-30T13:46:58Z http://eprints.utm.my/id/eprint/76645/ Supervised classification and improved filtering method for shoreline detection Zulkifle, F. A. Hassan, R. Othman, R. M. Sallow, A. B. QA75 Electronic computers. Computer science Shoreline monitoring is important to overcome the problems in the measurement of the shoreline. Recently, many researchers have directed attention to methods of predicting shoreline changes by the use of multispectral images. However, the images being captured tend to have several problems due to the weather. Therefore, identification of multi class features which includes vegetation and shoreline using multispectral satellite image is one of the challenges encountered in the detection of shoreline. An efficient framework using the near infrared–histogram equalisation and improved filtering method is proposed to enhance the detection of the shoreline in Tanjung Piai, Malaysia, by using SPOT-5 images. Sub-pixel edge detection and the Wallis filter are used to compute the edge location with the subpixel accuracy and reduce the noise. Then, the image undergoes image classification process by using Support Vector Machine. The proposed method performed more effectively and reliable in preserving the missing line of the shoreline edge in the SPOT-5 images. Asian Research Publishing Network 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/76645/1/RohayantiHassan2017_SupervisedClassificationandImprovedFiltering.pdf Zulkifle, F. A. and Hassan, R. and Othman, R. M. and Sallow, A. B. (2017) Supervised classification and improved filtering method for shoreline detection. Journal of Theoretical and Applied Information Technology, 95 (20). pp. 5628-5636. ISSN 1992-8645 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032821943&partnerID=40&md5=552fb86048646216e07b4392dcd221b2 http://www.jatit.org/volumes/Vol95No20/31Vol95No20.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zulkifle, F. A.
Hassan, R.
Othman, R. M.
Sallow, A. B.
Supervised classification and improved filtering method for shoreline detection
description Shoreline monitoring is important to overcome the problems in the measurement of the shoreline. Recently, many researchers have directed attention to methods of predicting shoreline changes by the use of multispectral images. However, the images being captured tend to have several problems due to the weather. Therefore, identification of multi class features which includes vegetation and shoreline using multispectral satellite image is one of the challenges encountered in the detection of shoreline. An efficient framework using the near infrared–histogram equalisation and improved filtering method is proposed to enhance the detection of the shoreline in Tanjung Piai, Malaysia, by using SPOT-5 images. Sub-pixel edge detection and the Wallis filter are used to compute the edge location with the subpixel accuracy and reduce the noise. Then, the image undergoes image classification process by using Support Vector Machine. The proposed method performed more effectively and reliable in preserving the missing line of the shoreline edge in the SPOT-5 images.
format Article
author Zulkifle, F. A.
Hassan, R.
Othman, R. M.
Sallow, A. B.
author_facet Zulkifle, F. A.
Hassan, R.
Othman, R. M.
Sallow, A. B.
author_sort Zulkifle, F. A.
title Supervised classification and improved filtering method for shoreline detection
title_short Supervised classification and improved filtering method for shoreline detection
title_full Supervised classification and improved filtering method for shoreline detection
title_fullStr Supervised classification and improved filtering method for shoreline detection
title_full_unstemmed Supervised classification and improved filtering method for shoreline detection
title_sort supervised classification and improved filtering method for shoreline detection
publisher Asian Research Publishing Network
publishDate 2017
url http://eprints.utm.my/id/eprint/76645/1/RohayantiHassan2017_SupervisedClassificationandImprovedFiltering.pdf
http://eprints.utm.my/id/eprint/76645/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032821943&partnerID=40&md5=552fb86048646216e07b4392dcd221b2
http://www.jatit.org/volumes/Vol95No20/31Vol95No20.pdf
_version_ 1643657369458049024
score 13.211869

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