Gelişmiş Arama

Basit öğe kaydını göster

dc.contributor.authorMatcı, Dilek Küçük
dc.contributor.authorAvdan, Uğur
dc.date.accessioned2020-07-09T20:52:11Z
dc.date.available2020-07-09T20:52:11Z
dc.date.issued2019
dc.identifier.issn2148-9173
dc.identifier.urihttp://doi.org/10.30897/ijegeo.466985
dc.identifier.urihttps://app.trdizin.gov.tr/publication/paper/detail/TXpNME5UWXdNQT09
dc.identifier.urihttps://hdl.handle.net/11421/23828
dc.description.abstractRemote sensing technologies provide very important big data to various science areas such as risk identification, damage detection and prevention studies. However, the classification processes used to create thematic maps to interpret this data can be ineffective due to the wide range of properties that these images provide. At this point, there arises a requirement to optimize the data. The first objective of this study is to evaluate the performance of the Bat Search Algorithm which has not previously been used for improving the classification accuracy of remotely sensed images by optimizing attributes. The second objective is to compare the performance of the Genetic Algorithm, Bat Search Algorithm, Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm, which are used in many areas of the literature for the optimization of the attributes of remotely sensed images. For these purposes, an image from the Landsat 8 satellite is used. The performance of the algorithms is compared by classifying the image using the K-Means method. The analysis shows a 10-22% increase in overall accuracy with the addition of attribute optimization.en_US
dc.language.isoengen_US
dc.relation.isversionof10.30897/ijegeo.466985en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiyoloji Çeşitliliğinin Korunmasıen_US
dc.subjectBiyolojien_US
dc.subjectEkolojien_US
dc.subjectÇevre Bilimlerien_US
dc.subjectOşinografien_US
dc.subjectSu Kaynaklarıen_US
dc.subjectJeokimya ve Jeofiziken_US
dc.subjectJeolojien_US
dc.subjectMeteoroloji ve Atmosferik Bilimleren_US
dc.subjectArkeolojien_US
dc.titleOptimization of Remote Sensing Image Attributes to Improve Classification Accuracyen_US
dc.typearticleen_US
dc.relation.journalInternational Journal of Environment and Geoinformaticsen_US
dc.contributor.departmentAnadolu Üniversitesien_US
dc.identifier.volume6en_US
dc.identifier.issue1en_US
dc.identifier.startpage50en_US
dc.identifier.endpage56en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US


Bu öğenin dosyaları:

Thumbnail

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster