Fast texture classification of denoised SAR image patches using GLCM on Spark

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dc.authoridOzcan, Caner/0000-0002-2854-4005
dc.contributor.authorOzcan, Caner
dc.contributor.authorErsoy, Okan
dc.contributor.authorOgul, Iskender Ulgen
dc.date.accessioned2024-09-29T16:08:20Z
dc.date.available2024-09-29T16:08:20Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
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.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK); TUBITAKen_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under 2219-International Postdoctoral Research Scholarship Programme. The authors thank TUBITAK for the financial and scientific support.en_US
dc.identifier.doi10.3906/elk-1904-7
dc.identifier.endpage195en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85079842229en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage182en_US
dc.identifier.trdizinid334578en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1904-7
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/334578
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7494
dc.identifier.volume28en_US
dc.identifier.wosWOS:000510459900013en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectmachine learningen_US
dc.subjectsynthetic aperture radaren_US
dc.subjectcluster computingen_US
dc.subjectnaive Bayesen_US
dc.subjectdecision treeen_US
dc.subjectrandom foresten_US
dc.titleFast texture classification of denoised SAR image patches using GLCM on Sparken_US
dc.typeArticleen_US

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