Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10370
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dc.contributor.authorÖzcan, Caner-
dc.contributor.authorErsoy, Okan-
dc.contributor.authorOğul, İskender Ülgen-
dc.date.accessioned2021-01-24T18:34:16Z-
dc.date.available2021-01-24T18:34:16Z-
dc.date.issued2020-
dc.identifier.issn1300-0632-
dc.identifier.issn1300-0632-
dc.identifier.urihttps://doi.org/10.3906/elk-1904-7-
dc.identifier.urihttps://hdl.handle.net/11147/10370-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/334578-
dc.description.abstractClassification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.en_US
dc.language.isoenen_US
dc.publisherTürkiye Klinikleri Journal of Medical Sciencesen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRandom forestsen_US
dc.subjectCuster computingen_US
dc.subjectSynthetic aperture radaren_US
dc.subjectMachine learningen_US
dc.titleFast texture classification of denoised SAR image patches using GLCM on Sparken_US
dc.typeArticleen_US
dc.institutionauthorOğul, İskender Ülgen-
dc.departmentİzmir Institute of Technology. Electrical and Electronics Engineeringen_US
dc.identifier.volume28en_US
dc.identifier.issue1en_US
dc.identifier.startpage182 - 195en_US
dc.identifier.wosWOS:000510459900013en_US
dc.identifier.scopus2-s2.0-85079842229en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.3906/elk-1904-7-
dc.relation.doi10.3906/elk-1904-7en_US
dc.coverage.doi10.3906/elk-1904-7en_US
dc.identifier.trdizinid334578en_US
dc.identifier.wosqualityQ4-
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept01. Izmir Institute of Technology-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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