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dc.contributor.authorBorna, Kambiz
dc.contributor.authorMoore, T.
dc.contributor.authorAzadeh, N.H.
dc.contributor.authorPascal, S.
dc.date.accessioned2022-06-21T21:59:10Z
dc.date.available2022-06-21T21:59:10Z
dc.date.issued2021-12-02
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10652/5724
dc.description.abstractUnsupervised image classification methods conventionally use the spatial information of pixels to reduce the effect of speckled noise in the classified map. To extract this spatial information, they employ a predefined geometry, i.e., a fixed-size window or segmentation map. However, this coding of geometry lacks the necessary complexity to accurately reflect the spatial connectivity within objects in a scene. Additionally, there is no unique mathematical formula to determine the shape and scale applied to the geometry, being parameters that are usually estimated by expert users. In this paper, a novel geometry-led approach using Vector Agents (VAs) is proposed to address the above drawbacks in unsupervised classification algorithms. Our proposed method has two primary steps: (1) creating reliable training samples and (2) constructing the VA model. In the first step, the method applies the statistical information of a classified image by k-means to select a set of reliable training samples. Then, in the second step, the VAs are trained and constructed to classify the image. The model is tested for classification on three high spatial resolution images. The results show the enhanced capability of the VA model to reduce noise in images that have complex features, e.g., streets, buildings.en_NZ
dc.language.isoenen_NZ
dc.publisherMDPI (Multidisciplinary Digital Publishing Institute)en_NZ
dc.relation.urihttps://www.mdpi.com/2072-4292/13/23/4896en_NZ
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectimage processingen_NZ
dc.subjectimage classificationen_NZ
dc.subjectunsupervised image classificationen_NZ
dc.subjectdynamic geometryen_NZ
dc.subjectvector agentsen_NZ
dc.subjectmodellingen_NZ
dc.titleUsing vector agents to implement an unsupervised image classification algorithmen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2022-06-18T14:30:04Z
dc.rights.holder© 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_NZ
dc.identifier.doidoi:https://doi.org/10.3390/rs13234896en_NZ
dc.subject.marsden460306 Image processingen_NZ
dc.identifier.bibliographicCitationBorna, K., Moore, T.,Azadeh, N.H., & Pascal, S. (2021). Using vector agents to implement an unsupervised image classification algorithm. Remote Sensing, 13, 4896. doi:https://doi.org/10.3390/rs13234896en_NZ
unitec.publication.spage4896en_NZ
unitec.publication.volume13en_NZ
unitec.publication.issue23en_NZ
unitec.publication.titleRemote Sensingen_NZ
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationUniversity of Otagoen_NZ
dc.contributor.affiliationFederation University Australiaen_NZ
unitec.identifier.roms68312en_NZ
unitec.publication.placeBasel, Switzerlanden_NZ
unitec.institution.studyareaComputingen_NZ


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