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Article: Neural networks in detection and identification of littoral oil pollution by remote sensing

TitleNeural networks in detection and identification of littoral oil pollution by remote sensing
Authors
Issue Date2004
PublisherSpringer Verlag
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, v. 3173, p. 977-982 How to Cite?
AbstractIn order to differentiate classes of oil-spills on water surface, a neural network (NN) approach is applied for spectral data analysis and identification of airborne laser fluorosensor in this paper. The target to be detected may be one of the following: seawater, lube, diesel, etc. The primary requirement for airborne sensors is to identify the substances targeted by the laser beam. Pearson Correlation Coefficient (PCC) method is one of the most current approaches. This paper outlines the NN model for the identification of the spilled oils, and makes a comparison with PCC in an effort to increase the level of confidence in the identification results. The results of ground tests using known targets show an increased confidence in the results when using the NN Model compared to that of PCC. It is believed that the NN model would play a significant role in the ocean oil-spill identification in the future. © Springer-Verlag 2004.
Persistent Identifierhttp://hdl.handle.net/10722/91132
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorLin, Ben_HK
dc.contributor.authorAn, Jen_HK
dc.contributor.authorBrown, Cen_HK
dc.contributor.authorZhang, Hen_HK
dc.date.accessioned2010-09-17T10:13:31Z-
dc.date.available2010-09-17T10:13:31Z-
dc.date.issued2004en_HK
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, v. 3173, p. 977-982en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/91132-
dc.description.abstractIn order to differentiate classes of oil-spills on water surface, a neural network (NN) approach is applied for spectral data analysis and identification of airborne laser fluorosensor in this paper. The target to be detected may be one of the following: seawater, lube, diesel, etc. The primary requirement for airborne sensors is to identify the substances targeted by the laser beam. Pearson Correlation Coefficient (PCC) method is one of the most current approaches. This paper outlines the NN model for the identification of the spilled oils, and makes a comparison with PCC in an effort to increase the level of confidence in the identification results. The results of ground tests using known targets show an increased confidence in the results when using the NN Model compared to that of PCC. It is believed that the NN model would play a significant role in the ocean oil-spill identification in the future. © Springer-Verlag 2004.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlagen_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleNeural networks in detection and identification of littoral oil pollution by remote sensingen_HK
dc.typeArticleen_HK
dc.identifier.emailLin, B:blin@hku.hken_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-35048822898en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-35048822898&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3173en_HK
dc.identifier.spage977en_HK
dc.identifier.epage982en_HK
dc.identifier.eissn1611-3349-

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