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Article: Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing

TitleNeural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing
Authors
KeywordsBack-Propagation (B-P)
Laser Fluorosensor Remote Sensing
Neural Network
Perceptron
Self-Organizing Feature Maps (Som)
Issue Date2003
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/app/Publications/index.cfm?fuseaction=proceedings
Citation
Proceedings of SPIE - The International Society for Optical Engineering, 2003, v. 4892, p. 336-346 How to Cite?
AbstractIn this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.
Persistent Identifierhttp://hdl.handle.net/10722/91150
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorLin, Ben_HK
dc.contributor.authorAn, Jen_HK
dc.contributor.authorBrown, CEen_HK
dc.contributor.authorChen, Wen_HK
dc.date.accessioned2010-09-17T10:13:47Z-
dc.date.available2010-09-17T10:13:47Z-
dc.date.issued2003en_HK
dc.identifier.citationProceedings of SPIE - The International Society for Optical Engineering, 2003, v. 4892, p. 336-346en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/91150-
dc.description.abstractIn this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.en_HK
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/app/Publications/index.cfm?fuseaction=proceedingsen_HK
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_HK
dc.subjectBack-Propagation (B-P)en_HK
dc.subjectLaser Fluorosensor Remote Sensingen_HK
dc.subjectNeural Networken_HK
dc.subjectPerceptronen_HK
dc.subjectSelf-Organizing Feature Maps (Som)en_HK
dc.titleNeural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensingen_HK
dc.typeArticleen_HK
dc.identifier.emailLin, B:blin@hku.hken_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.466789en_HK
dc.identifier.scopuseid_2-s2.0-0042168833en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0042168833&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4892en_HK
dc.identifier.spage336en_HK
dc.identifier.epage346en_HK

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