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Article: BP fusion model for the detection of oil spills on the sea by remote sensing

TitleBP fusion model for the detection of oil spills on the sea by remote sensing
Authors
KeywordsBp Nn
Edge Detection
Fusion
Remote Sensing
Issue Date2002
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, 2002, v. 4897, p. 360-370 How to Cite?
AbstractOil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills' image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason that we selected BP neural net as the fusion technology is that the relation between simple operators' result of edge gray level and the image's true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing of oil spill image's edge detection.
Persistent Identifierhttp://hdl.handle.net/10722/91099
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Wen_HK
dc.contributor.authorAn, Jen_HK
dc.contributor.authorZhang, Hen_HK
dc.contributor.authorLin, Ben_HK
dc.date.accessioned2010-09-17T10:13:01Z-
dc.date.available2010-09-17T10:13:01Z-
dc.date.issued2002en_HK
dc.identifier.citationProceedings of SPIE - The International Society for Optical Engineering, 2002, v. 4897, p. 360-370en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/91099-
dc.description.abstractOil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills' image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason that we selected BP neural net as the fusion technology is that the relation between simple operators' result of edge gray level and the image's true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing of oil spill image's edge detection.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.subjectBp Nnen_HK
dc.subjectEdge Detectionen_HK
dc.subjectFusionen_HK
dc.subjectRemote Sensingen_HK
dc.titleBP fusion model for the detection of oil spills on the sea by remote sensingen_HK
dc.typeArticleen_HK
dc.identifier.emailLin, B:blin@hku.hken_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.467327en_HK
dc.identifier.scopuseid_2-s2.0-0141494528en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0141494528&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4897en_HK
dc.identifier.spage360en_HK
dc.identifier.epage370en_HK

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