A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

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dc.authorid0000-0002-5159-0659en_US
dc.contributor.authorUçar, Ferhat
dc.contributor.authorKorkmaz, Deniz
dc.date.accessioned2022-03-30T08:59:14Z
dc.date.available2022-03-30T08:59:14Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractShip detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, the intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship detection. In this study, we propose a Deep Convolutional Neural Network (DCNN) model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image with a deep learning model built from scratch. The designed CNN model is constructed with a plain and easy to implement form in particular to the preferred satellite image set. Visual and graphical results show that the proposed model provides an efficient detection process with an accuracy of 99.60%.en_US
dc.identifier.citationUcar, F., & Korkmaz, D. (2020). A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images. Sakarya University Journal of Science, 24, 197-204.en_US
dc.identifier.doi10.16984/saufenbilder.587731
dc.identifier.endpage204en_US
dc.identifier.issn2147-835Xen_US
dc.identifier.issue1en_US
dc.identifier.startpage197en_US
dc.identifier.urihttp://www.saujs.sakarya.edu.tr/tr/
dc.identifier.urihttps://hdl.handle.net/20.500.12899/865
dc.identifier.volume24en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorKorkmaz, Deniz
dc.language.isoenen_US
dc.publisherSakarya Üniversitesien_US
dc.relation.ispartofSakarya University Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep convolutional neural networksen_US
dc.subjectship detectionen_US
dc.subjectremote sensingen_US
dc.subjectsatellite imageryen_US
dc.titleA Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Imagesen_US
dc.typeArticleen_US

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