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https://hdl.handle.net/1959.11/15641
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kumar, Lalit | en |
dc.contributor.author | Sinha, Priyakant | en |
dc.contributor.author | Taylor, Subhashni | en |
dc.date.accessioned | 2014-09-09T15:32:00Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Journal of Applied Remote Sensing, 8(1), p. 083616-1-083616-16 | en |
dc.identifier.issn | 1931-3195 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/15641 | - |
dc.description.abstract | The 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.language | en | en |
dc.publisher | International Society for Optical Engineering (SPIE) | en |
dc.relation.ispartof | Journal of Applied Remote Sensing | en |
dc.title | Improving image classification in a complex wetland ecosystem through image fusion techniques | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1117/1.JRS.8.083616 | en |
dc.subject.keywords | Natural Resource Management | en |
dc.subject.keywords | Photogrammetry and Remote Sensing | en |
dc.subject.keywords | Geospatial Information Systems | en |
local.contributor.firstname | Lalit | en |
local.contributor.firstname | Priyakant | en |
local.contributor.firstname | Subhashni | en |
local.subject.for2008 | 090903 Geospatial Information Systems | en |
local.subject.for2008 | 090905 Photogrammetry and Remote Sensing | en |
local.subject.for2008 | 050209 Natural Resource Management | en |
local.subject.seo2008 | 960802 Coastal and Estuarine Flora, Fauna and Biodiversity | en |
local.subject.seo2008 | 960604 Environmental Management Systems | en |
local.subject.seo2008 | 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments | en |
local.profile.school | School of Environmental and Rural Science | en |
local.profile.school | School of Science and Technology | en |
local.profile.school | School of Education | en |
local.profile.email | lkumar@une.edu.au | en |
local.profile.email | psinha2@une.edu.au | en |
local.profile.email | btaylo26@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.identifier.epublicationsrecord | une-20140905-172210 | en |
local.publisher.place | United States of America | en |
local.identifier.runningnumber | 083616 | en |
local.format.startpage | 083616-1 | en |
local.format.endpage | 083616-16 | en |
local.identifier.scopusid | 84905750174 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 8 | en |
local.identifier.issue | 1 | en |
local.contributor.lastname | Kumar | en |
local.contributor.lastname | Sinha | en |
local.contributor.lastname | Taylor | en |
dc.identifier.staff | une-id:lkumar | en |
dc.identifier.staff | une-id:psinha2 | en |
dc.identifier.staff | une-id:btaylo26 | en |
local.profile.orcid | 0000-0002-9205-756X | en |
local.profile.orcid | 0000-0002-1624-0901 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:15877 | en |
local.identifier.handle | https://hdl.handle.net/1959.11/15641 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Improving image classification in a complex wetland ecosystem through image fusion techniques | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Kumar, Lalit | en |
local.search.author | Sinha, Priyakant | en |
local.search.author | Taylor, Subhashni | en |
local.uneassociation | Unknown | en |
local.year.published | 2014 | en |
local.subject.for2020 | 401302 Geospatial information systems and geospatial data modelling | en |
local.subject.for2020 | 401304 Photogrammetry and remote sensing | en |
local.subject.for2020 | 410406 Natural resource management | en |
local.subject.seo2020 | 180203 Coastal or estuarine biodiversity | en |
local.subject.seo2020 | 189999 Other environmental management not elsewhere classified | en |
local.subject.seo2020 | 180601 Assessment and management of terrestrial ecosystems | en |
Appears in Collections: | Journal Article School of Environmental and Rural Science |
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