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Article: Factors affecting spatial variation of classification uncertainty in an image object-based vegetation mapping

TitleFactors affecting spatial variation of classification uncertainty in an image object-based vegetation mapping
Authors
Issue Date2008
Citation
Photogrammetric Engineering and Remote Sensing, 2008, v. 74, n. 8, p. 1007-1018 How to Cite?
AbstractMuch effort has been spent on examining the spatial variation of classification accuracy and associated factors on a per-pixel basis. In the past few years, object-based classification has attracted growing interest. This paper examines factors affecting the spatial variation of classification uncertainty in an object-based vegetation mapping. We studied six categories of factors in an object-based classification: general membership, topography, sample object density, spatial composition, sample object reliability, and object features. First, classification uncertainty (classification accuracy on a per-case basis) is derived with a bootstrap method. Then, six categories of factors are quantified by categorical or continuous variables. In this step, the appropriate radius for calculating the spatial composition metrics of sample objects is also discussed. Finally, classification uncertainty is modeled as a function of those factors using a mixed linear model. The significant factors are identified and their parameters are estimated from restricted maximum likelihood fit. The modeling results show that elevation, sample object size, sample object reliability, sample object density, and sample spatial composition significantly influence the object-based classification uncertainty. Many of these factors are closely related to the object-based approach. The result of this study helps in understanding classification errors and suggests further improvement of the classification. © 2008 American Society for Photogrammetry and Remote Sensing.
Persistent Identifierhttp://hdl.handle.net/10722/296629
ISSN
2023 Impact Factor: 1.0
2023 SCImago Journal Rankings: 0.309
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, Qian-
dc.contributor.authorGong, Peng-
dc.contributor.authorTian, Yong Q.-
dc.contributor.authorPu, Ruiliang-
dc.contributor.authorYang, Jun-
dc.date.accessioned2021-02-25T15:16:18Z-
dc.date.available2021-02-25T15:16:18Z-
dc.date.issued2008-
dc.identifier.citationPhotogrammetric Engineering and Remote Sensing, 2008, v. 74, n. 8, p. 1007-1018-
dc.identifier.issn0099-1112-
dc.identifier.urihttp://hdl.handle.net/10722/296629-
dc.description.abstractMuch effort has been spent on examining the spatial variation of classification accuracy and associated factors on a per-pixel basis. In the past few years, object-based classification has attracted growing interest. This paper examines factors affecting the spatial variation of classification uncertainty in an object-based vegetation mapping. We studied six categories of factors in an object-based classification: general membership, topography, sample object density, spatial composition, sample object reliability, and object features. First, classification uncertainty (classification accuracy on a per-case basis) is derived with a bootstrap method. Then, six categories of factors are quantified by categorical or continuous variables. In this step, the appropriate radius for calculating the spatial composition metrics of sample objects is also discussed. Finally, classification uncertainty is modeled as a function of those factors using a mixed linear model. The significant factors are identified and their parameters are estimated from restricted maximum likelihood fit. The modeling results show that elevation, sample object size, sample object reliability, sample object density, and sample spatial composition significantly influence the object-based classification uncertainty. Many of these factors are closely related to the object-based approach. The result of this study helps in understanding classification errors and suggests further improvement of the classification. © 2008 American Society for Photogrammetry and Remote Sensing.-
dc.languageeng-
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensing-
dc.titleFactors affecting spatial variation of classification uncertainty in an image object-based vegetation mapping-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.14358/PERS.74.8.1007-
dc.identifier.scopuseid_2-s2.0-50249096184-
dc.identifier.volume74-
dc.identifier.issue8-
dc.identifier.spage1007-
dc.identifier.epage1018-
dc.identifier.isiWOS:000258213100010-
dc.identifier.issnl0099-1112-

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