Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation

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dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridShabaz, Mohammad/0000-0001-5106-7609
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidShabaz, Mohammad/AAB-3168-2020
dc.contributor.authorChen, Zhuoran
dc.contributor.authorCong, Biao
dc.contributor.authorHua, Zhenxing
dc.contributor.authorCengiz, Korhan
dc.contributor.authorShabaz, Mohammad
dc.date.accessioned2024-06-12T10:58:01Z
dc.date.available2024-06-12T10:58:01Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractIn synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.en_US
dc.description.sponsorshipInnovation fund of industry-university-research center for science and technology development of the ministry of education in 2018 Key technologies of smart campus system based on Internet of things in the era of Internet + education [2018A02002]; Research project of boda college of jilin normal university in 2019 Intelligent monitoring system of motorist's heart rate based on big data [2019BD002]en_US
dc.description.sponsorshipInnovation fund of industry-university-research center for science and technology development of the ministry of education in 2018 Key technologies of smart campus system based on Internet of things in the era of Internet + education (2018A02002); Research project of boda college of jilin normal university in 2019 Intelligent monitoring system of motorist's heart rate based on big data (2019BD002).en_US
dc.identifier.doi10.1515/jisys-2021-0096
dc.identifier.endpage1025en_US
dc.identifier.issn0334-1860
dc.identifier.issn2191-026X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85118225520en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1014en_US
dc.identifier.urihttps://doi.org/10.1515/jisys-2021-0096
dc.identifier.urihttps://hdl.handle.net/20.500.14551/19890
dc.identifier.volume30en_US
dc.identifier.wosWOS:000720948500014en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWalter De Gruyter Gmbhen_US
dc.relation.ispartofJournal Of Intelligent Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClustering Algorithmen_US
dc.subjectFarmlanden_US
dc.subjectSAR Image Segmentationen_US
dc.subjectRegional Algorithmsen_US
dc.subjectNoise Interferenceen_US
dc.subjectSaren_US
dc.titleApplication of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentationen_US
dc.typeArticleen_US

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