Automatic determination of different soil types via several machine learning algorithms employing radarsat-2 SAR image polarization coefficients

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dc.contributor.authorAcar, Emrullah
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.contributor.orcid0000-0002-1897-9830
dc.contributor.orcid0000-0002-9368-8902
dc.date.accessioned2024-04-24T17:56:03Z
dc.date.available2024-04-24T17:56:03Z
dc.date.issued2022
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractSynthetic aperture radar (SAR), which is one of the most popular remote sensing technologies, has been extensively employed for classification of various soil types, soil texture description, and its mapping. Determining the soil type is useful for rural and urban management. In the current study, several machine learning algorithms, which consist of the K-Nearest Neighbor (K-NN), Extreme Learning Machine (ELM), and Naive Bayes (NB), have been recommended by utilizing Radarsat-2 SAR data. A pilot region in the city of Diyarbakir, Turkey that spreads among 370 46’- 380 04’ N latitudes and 400 04’- 400 26’E longitudes was employed, and nearly, 156 soil samples were collected for classification of two soil types (Clayey and Clayey+Loamy). After that, four different Radarsat-2 SAR image polarization coefficients were computed for each soil sample, and these coefficients were utilized as inputs in the classification stage. Finally, the results showed that an overall accuracy of 91.1% with K-NN, 82.0% with ELM, and 85.2% with NB algorithm was computed for the classification of two soil types.en_US
dc.description.sponsorship114E543; European Space Agency, ESAen_US
dc.description.sponsorshipThe authors would like to thank European Space Agency for Sentinel-1 Toolbox and Dicle University Science and Technology Application and Research Center (DUBTAM) for soil sample measurements. This work was supported by TUBITAK 1001 (No. 114E543) research project.en_US
dc.description.sponsorshipAcknowledgments The authors would like to thank European Space Agency for Sentinel-1 Toolbox and Dicle University Science and Technology Application and Research Center (DUBTAM) for soil sample measurements. This work was supported by TUBITAK 1001 (No. 114E543) research project.en_US
dc.identifier.citationAcar, E. ve Özerdem, M. S. (2022). Automatic determination of different soil types via several machine learning algorithms employing radarsat-2 SAR image polarization coefficients. Springer Optimization and Its Applications, 199, 219-233.
dc.identifier.doi10.1007/978-3-031-21225-3_9
dc.identifier.endpage233en_US
dc.identifier.issn1931-6828
dc.identifier.scopus2-s2.0-85152283720
dc.identifier.scopusqualityQ4
dc.identifier.startpage219en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-21225-3_9
dc.identifier.urihttps://hdl.handle.net/11468/23217
dc.identifier.volume199en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSpringer Optimization and Its Applications
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleAutomatic determination of different soil types via several machine learning algorithms employing radarsat-2 SAR image polarization coefficientsen_US
dc.titleAutomatic determination of different soil types via several machine learning algorithms employing radarsat-2 SAR image polarization coefficients
dc.typeBook Chapteren_US

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