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A Dempster-Shafer Relaxation Approach to Context Classification

Richards, John; Jia, Xiuping

Description

A relaxation scheme is proposed in which Dempster-Shafer evidential theory is used to bring the effect of the spatial neighborhood of a pixel into a classification. The benefits include the ability to incorporate uncertainty in the neighborhood information, allowing a stopping criterion to be devised based on increasing the uncertainty contribution of the neighborhood to unity within a prescribed number of iterations. The number of iterations to be used is governed by several factors, including...[Show more]

dc.contributor.authorRichards, John
dc.contributor.authorJia, Xiuping
dc.date.accessioned2015-12-07T22:53:04Z
dc.identifier.issn0196-2892
dc.identifier.urihttp://hdl.handle.net/1885/27694
dc.description.abstractA relaxation scheme is proposed in which Dempster-Shafer evidential theory is used to bring the effect of the spatial neighborhood of a pixel into a classification. The benefits include the ability to incorporate uncertainty in the neighborhood information, allowing a stopping criterion to be devised based on increasing the uncertainty contribution of the neighborhood to unity within a prescribed number of iterations. The number of iterations to be used is governed by several factors, including an estimate of how far out in the neighborhood pixels are assumed to be influential. As with standard relaxation labeling, but unlike many other context-sensitive methods, the evidential approach can be initialized from the results of a separate point statistical classification of the image; it is also consistent with multisource analyses based on evidential methods for fusion. A variation of evidential relaxation using considerably simplified neighborhood information is also developed, illustrating that very good results can be obtained without detailed knowledge of the spatial properties of a scene. The new procedures are compared experimentally with standard probabilistic relaxation and the application of Markov random fields.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Geoscience and Remote Sensing
dc.subjectKeywords: Dempster Shafer; Evidential theory; Markov random fields (MRF); Spatial context; Thematic mapping; Conformal mapping; Iterative methods; Markov processes; Pixels; Statistical methods; Uncertainty analysis; Image classification Dempster-Shafer; Evidence; Markov random fields (MRFs); Relaxation; Spatial context; Thematic mapping
dc.titleA Dempster-Shafer Relaxation Approach to Context Classification
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume45
dc.date.issued2007
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.absfor090905 - Photogrammetry and Remote Sensing
local.identifier.ariespublicationu3594520xPUB53
local.type.statusPublished Version
local.contributor.affiliationRichards, John, College of Engineering and Computer Science, ANU
local.contributor.affiliationJia, Xiuping, University of New South Wales
local.description.embargo2037-12-31
local.bibliographicCitation.issue5
local.bibliographicCitation.startpage1422
local.bibliographicCitation.lastpage1431
local.identifier.doi10.1109/TGRS.2007.893821
dc.date.updated2015-12-07T12:36:14Z
local.identifier.scopusID2-s2.0-34247471770
CollectionsANU Research Publications

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