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Oil spill detection and characterization from satellite image using artificial neural network algorithm

This paper describes the use of artificial neural network to identify and characterize oil spill acquired from satellite imagery. The objective of the algorithm is to classify every pixel of the image whether it is sea water or oil based on its intensity. In order to test the algorithm, several orde...

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Main Authors: Ridha, S., Wardaya, P.D.
Format: Conference or Workshop Item
Published: Society of Petroleum Engineers 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926174076&doi=10.2118%2f170406-ms&partnerID=40&md5=9fb6b8cc6d20554660e885be498d21e2
http://eprints.utp.edu.my/31775/
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spelling my.utp.eprints.317752022-03-29T03:37:02Z Oil spill detection and characterization from satellite image using artificial neural network algorithm Ridha, S. Wardaya, P.D. This paper describes the use of artificial neural network to identify and characterize oil spill acquired from satellite imagery. The objective of the algorithm is to classify every pixel of the image whether it is sea water or oil based on its intensity. In order to test the algorithm, several order of noise is introduced, simulating real situation caused by weather or other pollutant during the acquisition. The study shows that neural network algorithm provides efficient technique to differentiate the oil spill from water body. The neural network is capable of delivering accurate classification in less than one minute computation time. The required training data can also be suppressed indicating the cost effectiveness in term of computer memory. From the classification result, area prediction is performed by using standard image analysis. The technique counts the number of pixels classified as the oil and sum them to provide the areal extent of the spillage. This result gives important solution for environmental handling of the oil exploitation activity. For example, the method can be used in providing estimate on the thickness of the discharged oil so that environmental effect can be predicted more reliably. Copyright © 2014, Society of Petroleum Engineers. Society of Petroleum Engineers 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926174076&doi=10.2118%2f170406-ms&partnerID=40&md5=9fb6b8cc6d20554660e885be498d21e2 Ridha, S. and Wardaya, P.D. (2014) Oil spill detection and characterization from satellite image using artificial neural network algorithm. In: UNSPECIFIED. http://eprints.utp.edu.my/31775/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper describes the use of artificial neural network to identify and characterize oil spill acquired from satellite imagery. The objective of the algorithm is to classify every pixel of the image whether it is sea water or oil based on its intensity. In order to test the algorithm, several order of noise is introduced, simulating real situation caused by weather or other pollutant during the acquisition. The study shows that neural network algorithm provides efficient technique to differentiate the oil spill from water body. The neural network is capable of delivering accurate classification in less than one minute computation time. The required training data can also be suppressed indicating the cost effectiveness in term of computer memory. From the classification result, area prediction is performed by using standard image analysis. The technique counts the number of pixels classified as the oil and sum them to provide the areal extent of the spillage. This result gives important solution for environmental handling of the oil exploitation activity. For example, the method can be used in providing estimate on the thickness of the discharged oil so that environmental effect can be predicted more reliably. Copyright © 2014, Society of Petroleum Engineers.
format Conference or Workshop Item
author Ridha, S.
Wardaya, P.D.
spellingShingle Ridha, S.
Wardaya, P.D.
Oil spill detection and characterization from satellite image using artificial neural network algorithm
author_facet Ridha, S.
Wardaya, P.D.
author_sort Ridha, S.
title Oil spill detection and characterization from satellite image using artificial neural network algorithm
title_short Oil spill detection and characterization from satellite image using artificial neural network algorithm
title_full Oil spill detection and characterization from satellite image using artificial neural network algorithm
title_fullStr Oil spill detection and characterization from satellite image using artificial neural network algorithm
title_full_unstemmed Oil spill detection and characterization from satellite image using artificial neural network algorithm
title_sort oil spill detection and characterization from satellite image using artificial neural network algorithm
publisher Society of Petroleum Engineers
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926174076&doi=10.2118%2f170406-ms&partnerID=40&md5=9fb6b8cc6d20554660e885be498d21e2
http://eprints.utp.edu.my/31775/
_version_ 1738657294742192128
score 13.244414

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