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dc.contributor.authorYılmaz, Volkan
dc.date.accessioned2021-02-16T10:56:05Z
dc.date.available2021-02-16T10:56:05Z
dc.date.issued2021en_US
dc.identifier.citationYılmaz, V. (2021). Investigation of the performances of advanced image classification‐based ground filtering approaches for digital terrain model generation. Concurrency and Computation: Practice and Experience, e6219.en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.6219
dc.identifier.urihttps://hdl.handle.net/11494/2630
dc.description.abstractThe majority of the ground filtering techniques proposed so far use several user-defined parameter values. Since no standard protocols exist to define these parameters, obtaining the optimum filtering performance is very hard, especially in large-extent areas with abrupt topography changes. This, of course, reveals the necessity of some more efficient strategies to ease the ground filtering process in such areas. Utilizing classified images for ground filtering purpose may be of help to achieve this. Hence, this study, for the first time in the literature, investigated the performances of the state-of-the-art machine learning algorithms maximum likelihood (ML), artificial neural network (ANN), support vector machines (SVM), and random forest (RF) in ground filtering of a UAS-based point cloud. The used approaches were based on the assignment of the points corresponding to the ground-related classes to the ground class. Evaluations showed that the SVM-based ground filtering approach achieved the optimum filtering result. The SVM-, ML-, RF-, and ANN-based ground filtering methods achieved the total errors of 13.2%, 16.4%, 19.6%, and 21.9% in the test site, respectively.en_US
dc.language.isoengen_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDigital terrain modelen_US
dc.subjectGround filteringen_US
dc.subjectİmage classificationen_US
dc.subjectSupport vector machinesen_US
dc.subjectUnmannedaerial systemsen_US
dc.titleInvestigation of the performances of advanced image classification-based ground filtering approaches for digital terrain model generationen_US
dc.typearticleen_US
dc.relation.journalConcurrency Computationen_US
dc.departmentAÇÜ, Artvin Meslek Yüksekokuluen_US
dc.authorid0000-0003-0685-8369en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1002/cpe.6219en_US
dc.contributor.institutionauthorYılmaz, Volkanen_US


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