Development of Image Processing Based Solution for Vegetation Classification Using GLCM & Neural Network

Research output: Contribution to journalArticle

Abstract

Trees and vegetation encroachment in the right-of-way of transmission lines has become a major issue that affect the reliability of National grid system. However, current practice to monitor and control the vegetation encroachment along the transmission line is ineffective, costly and time consuming with pole climbing, foot patrolling or inspection using vehicle. Automation monitoring system is essential for more efficiency and less time consuming. This paper is a report on the process of trees identification using web mapping services application through Google Map to monitor vegetation activity. The main
purpose is to identify vegetation and danger trees along the transmission right of ways that caused to electricity supply interruptions. The method used is Gray Level Co-occurrence Matrix (GLCM) to detailing the image characteristics, extracting the features value and classified them into each classes of vegetation. From the GLCM data matrices, a total of 20 Features extracted in statistical method by Haralick and some new features by David A. Clausi and L.K. Soh were
applied to analyze the vegetation image.
Original languageEnglish
Pages (from-to)88-97
Number of pages10
JournalThe International Journal of Science & Technoledge
Volume5
Issue number7
Publication statusPublished - Jul 2017

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Image processing
Neural networks
Rights of way
Electric lines
Poles
Statistical methods
Automation
Electricity
Inspection
Monitoring

Cite this

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title = "Development of Image Processing Based Solution for Vegetation Classification Using GLCM & Neural Network",
abstract = "Trees and vegetation encroachment in the right-of-way of transmission lines has become a major issue that affect the reliability of National grid system. However, current practice to monitor and control the vegetation encroachment along the transmission line is ineffective, costly and time consuming with pole climbing, foot patrolling or inspection using vehicle. Automation monitoring system is essential for more efficiency and less time consuming. This paper is a report on the process of trees identification using web mapping services application through Google Map to monitor vegetation activity. The mainpurpose is to identify vegetation and danger trees along the transmission right of ways that caused to electricity supply interruptions. The method used is Gray Level Co-occurrence Matrix (GLCM) to detailing the image characteristics, extracting the features value and classified them into each classes of vegetation. From the GLCM data matrices, a total of 20 Features extracted in statistical method by Haralick and some new features by David A. Clausi and L.K. Soh wereapplied to analyze the vegetation image.",
author = "{V. Janahiraman}, Tiagrajah and {Tuan Abdullah}, {Tuan Ab Rashid} and Ramli, {Ahmad Qisti} and Norfahani Miskun",
year = "2017",
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journal = "The International Journal of Science & Technoledge",
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AU - V. Janahiraman, Tiagrajah

AU - Tuan Abdullah, Tuan Ab Rashid

AU - Ramli, Ahmad Qisti

AU - Miskun, Norfahani

PY - 2017/7

Y1 - 2017/7

N2 - Trees and vegetation encroachment in the right-of-way of transmission lines has become a major issue that affect the reliability of National grid system. However, current practice to monitor and control the vegetation encroachment along the transmission line is ineffective, costly and time consuming with pole climbing, foot patrolling or inspection using vehicle. Automation monitoring system is essential for more efficiency and less time consuming. This paper is a report on the process of trees identification using web mapping services application through Google Map to monitor vegetation activity. The mainpurpose is to identify vegetation and danger trees along the transmission right of ways that caused to electricity supply interruptions. The method used is Gray Level Co-occurrence Matrix (GLCM) to detailing the image characteristics, extracting the features value and classified them into each classes of vegetation. From the GLCM data matrices, a total of 20 Features extracted in statistical method by Haralick and some new features by David A. Clausi and L.K. Soh wereapplied to analyze the vegetation image.

AB - Trees and vegetation encroachment in the right-of-way of transmission lines has become a major issue that affect the reliability of National grid system. However, current practice to monitor and control the vegetation encroachment along the transmission line is ineffective, costly and time consuming with pole climbing, foot patrolling or inspection using vehicle. Automation monitoring system is essential for more efficiency and less time consuming. This paper is a report on the process of trees identification using web mapping services application through Google Map to monitor vegetation activity. The mainpurpose is to identify vegetation and danger trees along the transmission right of ways that caused to electricity supply interruptions. The method used is Gray Level Co-occurrence Matrix (GLCM) to detailing the image characteristics, extracting the features value and classified them into each classes of vegetation. From the GLCM data matrices, a total of 20 Features extracted in statistical method by Haralick and some new features by David A. Clausi and L.K. Soh wereapplied to analyze the vegetation image.

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