Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery

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dc.creatorSzabó, Loránd
dc.creatorAbriha, Dávid
dc.creatorPhinzi, Kwanele
dc.creatorSzabó, Szilárd
dc.date2021-07-14
dc.descriptionIn this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.en-US
dc.formatapplication/pdf
dc.identifierhttps://ojs.lib.unideb.hu/landsenv/article/view/8358
dc.identifier10.21120/LE/15/1/9
dc.languageeng
dc.publisherUniversity of Debrecen, Institute of Earth Sciencesen-US
dc.relationhttps://ojs.lib.unideb.hu/landsenv/article/view/8358/8708
dc.rightsCopyright (c) 2021 Landscape & Environmenten-US
dc.sourceLandscape & Environment; Vol. 15 No. 1 (2021); 66-75en-US
dc.source1789-7556
dc.source1789-4921
dc.subjectvegetation classificationen-US
dc.subjecthigh-resolutionen-US
dc.subjectsatellite imageryen-US
dc.subjectPlaneten-US
dc.subjectSkySaten-US
dc.subjecturban vegetationen-US
dc.subjectNDVIen-US
dc.subjectROC curveen-US
dc.subjectclassification performanceen-US
dc.subjectRandom Foresten-US
dc.subjectSupport Vector Machineen-US
dc.titleUrban vegetation classification with high-resolution PlanetScope and SkySat multispectral imageryen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
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Acta Geographica Debrecina Landscape & Environment