Support vector machine (SVM) and object based classification in earth linear features extraction: A comparison

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dc.contributor.authorSalleh, Siti Aekba
dc.contributor.authorKhalid, Nafisah
dc.contributor.authorDanny, Natasha
dc.contributor.authorZaki, Nurul Ain Mohd.
dc.contributor.authorÜstüner, Mustafa
dc.contributor.authorAbd Latif, Zulkiflee
dc.contributor.authorForonda, Vladimir
dc.date.accessioned2024-12-11T13:50:40Z
dc.date.available2024-12-11T13:50:40Z
dc.date.issued2024
dc.departmentAÇÜ, Mühendislik Fakültesi, Harita Mühendisliği Bölümüen_US
dc.description.abstractDue to the spectral and spatial properties of pervious and impervious surfaces, image classification and information extraction in detailed, small-scale mapping of urban surface materials is quite difficult and complex. Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques, which are fundamental to this approach. Consequently, the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods, pixel-based approach and object-based approach, using WorldView-2 satellite imagery to specifically highlight linear features such as roads, building edges, and road dividers. Two applied algorithms, including support vector machines (SVM) and ruled-based, were evaluated using two distinct software. A comparison of the results reveals that the object-based classification has a higher overall resolution than the pixel-based classification. The output of rule-based classification was satisfactory, with an overall accuracy of 88.6% (ENVI) and 92.2% (e-Cognition). The SVM classification result contained misclassified impervious surfaces and other urban features, as well as mixed objects. This classification achieved an overall accuracy of 75.1%. Nonetheless, this study provides an excellent overview for understanding the differences in their performances on the same data, as well as a comparison of the software employed.
dc.identifier.doi10.32604/rig.2024.050723
dc.identifier.endpage199en_US
dc.identifier.issn1260-5875
dc.identifier.startpage183en_US
dc.identifier.urihttp://dx.doi.org/10.32604/rig.2024.050723
dc.identifier.urihttps://hdl.handle.net/11494/5199
dc.identifier.volume33en_US
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.relation.ispartofREVUE INTERNATIONALE DE GEOMATIQUE
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.subjectRemote Sensingen_US
dc.subjectImage Classificationen_US
dc.titleSupport vector machine (SVM) and object based classification in earth linear features extraction: A comparisonen_US
dc.typeArticle

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