An advanced classification method for urban land cover classification

A tétel rövid nézete
dc.contributor.authorGuizani, Douraied
dc.contributor.authorBuday-Bódi, Erika
dc.contributor.authorTamás, János
dc.contributor.authorNagy, Attila
dc.date.accessioned2024-07-22T19:41:02Z
dc.date.available2024-07-22T19:41:02Z
dc.date.issued2024-06-03
dc.description.abstractThis manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features. Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.en
dc.formatapplication/pdf
dc.identifier.citationActa Agraria Debreceniensis, No. 1 (2024) , 51-57
dc.identifier.doihttps://doi.org/10.34101/actaagrar/1/13652
dc.identifier.issn2416-1640
dc.identifier.issue1
dc.identifier.jatitleActa agrar. Debr.
dc.identifier.jtitleActa Agraria Debreceniensis
dc.identifier.urihttps://hdl.handle.net/2437/375849
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/actaagrar/article/view/13652
dc.rights.accessOpen Access
dc.rights.ownerby the Author(s)
dc.subjectLand cover classificationen
dc.subjectLandsat8en
dc.subjectAccuracy Assessmenten
dc.subjectRemote Sensingen
dc.subjectLandscape Dynamicsen
dc.titleAn advanced classification method for urban land cover classificationen
dc.typefolyóiratcikkhu
dc.typearticleen
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