Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/15641
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dc.contributor.authorKumar, Laliten
dc.contributor.authorSinha, Priyakanten
dc.contributor.authorTaylor, Subhashnien
dc.date.accessioned2014-09-09T15:32:00Z
dc.date.issued2014en
dc.identifier.citationJournal of Applied Remote Sensing, 8(1), p. 083616-1-083616-16en
dc.identifier.issn1931-3195en
dc.identifier.urihttps://hdl.handle.net/1959.11/15641en
dc.description.abstractThe aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram-Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification (MLC) and support vector machine (SVM) methods. Gram-Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram-Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies.en
dc.languageenen
dc.publisherSPIE: International Society for Optical Engineeringen
dc.relation.ispartofJournal of Applied Remote Sensingen
dc.titleImproving image classification in a complex wetland ecosystem through image fusion techniquesen
dc.typeJournal Articleen
dc.identifier.doi10.1117/1.JRS.8.083616en
dc.subject.keywordsNatural Resource Managementen
dc.subject.keywordsPhotogrammetry and Remote Sensingen
dc.subject.keywordsGeospatial Information Systemsen
local.contributor.firstnameLaliten
local.contributor.firstnamePriyakanten
local.contributor.firstnameSubhashnien
local.subject.for2008090903 Geospatial Information Systemsen
local.subject.for2008090905 Photogrammetry and Remote Sensingen
local.subject.for2008050209 Natural Resource Managementen
local.subject.seo2008960802 Coastal and Estuarine Flora, Fauna and Biodiversityen
local.subject.seo2008960604 Environmental Management Systemsen
local.subject.seo2008960503 Ecosystem Assessment and Management of Coastal and Estuarine Environmentsen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Educationen
local.profile.emaillkumar@une.edu.auen
local.profile.emailpsinha2@une.edu.auen
local.profile.emailbtaylo26@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20140905-172210en
local.publisher.placeUnited States of Americaen
local.identifier.runningnumber083616en
local.format.startpage083616-1en
local.format.endpage083616-16en
local.peerreviewedYesen
local.identifier.volume8en
local.identifier.issue1en
local.contributor.lastnameKumaren
local.contributor.lastnameSinhaen
local.contributor.lastnameTayloren
dc.identifier.staffune-id:lkumaren
dc.identifier.staffune-id:psinha2en
dc.identifier.staffune-id:btaylo26en
local.profile.orcid0000-0002-9205-756Xen
local.profile.orcid0000-0002-1624-0901en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:15877en
local.identifier.handlehttps://hdl.handle.net/1959.11/15641en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleImproving image classification in a complex wetland ecosystem through image fusion techniquesen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.description.statisticsepubsVisitors: 338<br />Views: 487<br />Downloads: 0en
local.search.authorKumar, Laliten
local.search.authorSinha, Priyakanten
local.search.authorTaylor, Subhashnien
Appears in Collections:Journal Article
School of Environmental and Rural Science
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