A novel ship classification network with cascade deep features for line-of-sight sea data

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dc.authorid0000-0002-5159-0659en_US
dc.contributor.authorUcar, Ferhat
dc.contributor.authorKorkmaz, Deniz
dc.date.accessioned2022-03-22T13:08:10Z
dc.date.available2022-03-22T13:08:10Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn ship classifcation, selecting distinctive features and designing a proper classifer are two key points of the process. As a lack of most of the studies, these two essential points are considered separately. In this study, our proposal includes joint feature extraction, selection, and classifer design framework to build a novel deep cascade network for ship classifcation. We propose a transfer learning-based deep feature extraction using cascade Convolutional Neural Network architecture to convert the input image to multi-dimensional feature maps. The distributions of the MUTual Information (MUTInf) based feature selection algorithm compose a distinctive feature set originated for a public ship imagery dataset. The dataset consists of fve specifc classes of ships most existed in the maritime domain. A quadratic kernel-based non-linear Support Vector Machine is the designed classifer. Extensive experiments on the benchmark dataset indicate that the proposed framework can integrate the optimal feature set and a well-designed classifer to increase the performance of the classifcation process in ship imagery. In the experiments, the proposed method achieves an overall accuracy of 95.06%. The ship classes are also performed high classifcation performances into cargo, military, carrier, cruise, and tanker with an accuracy of 88.26%, 98.38%, 98.38%, 98.78%, and 91.50%, respectively. In addition, MUTInf feature selection reduces the features at a rate of 50.04%. These results show that the proposed method provides the highest performance value with less number of elements and outperforms state-of-the-art methodsen_US
dc.identifier.citationUcar, F., & Korkmaz, D. (2021). A novel ship classification network with cascade deep features for line-of-sight sea data. Machine Vision and Applications, 32(3), 1-15.en_US
dc.identifier.doi10.1007/s00138-021-01198-2
dc.identifier.endpage15en_US
dc.identifier.issue73en_US
dc.identifier.scopus2-s2.0-85104593150en_US
dc.identifier.startpage8en_US
dc.identifier.urihttps://doi.org/10.1007/s00138-021-01198-2
dc.identifier.urihttps://hdl.handle.net/20.500.12899/777
dc.identifier.volume32en_US
dc.identifier.wosWOS:000642410100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKorkmaz, Deniz
dc.language.isoenen_US
dc.relation.echttps://doi.org/10.1007/s00138-021-01198-2
dc.relation.ispartofMachine Vision and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectShip classifcationen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep feature extractionen_US
dc.subjectFeature selectionen_US
dc.subjectMutual informationen_US
dc.titleA novel ship classification network with cascade deep features for line-of-sight sea dataen_US
dc.typeArticleen_US

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