UC-merced image classification with CNN feature reduction using wavelet entropy optimized with genetic algorithm

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dc.authorid0000-0002-8611-701Xen_US
dc.contributor.authorÖzyurt, Fatih
dc.contributor.authorAva, Engin
dc.contributor.authorSert, Eser
dc.date.accessioned2021-06-08T12:56:28Z
dc.date.available2021-06-08T12:56:28Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe classification of high-resolution and remote sensed terrain images with high accuracy is one of the greatest challenges in machine learning. In the present study, a novel CNN feature reduction using Wavelet Entropy Optimized with Genetic Algorithm (GA-WEE-CNN) method was used for remote sensing images classification. The optimal wavelet family and optimal value of the parameters of the Wavelet Sure Entropy (WSE), Wavelet Nom Entropy (WNE), and Wavelet Threshold Entropy (WTE) were calculated, and given to classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The efficiency of the proposed hybrid method was tested using the UC-Merced dataset. 80% of the data were used as training data, and a performance rate of 98.8% was achieved with SVM classifier, which has been the highest ratio compared to all studies using same dataset so far with only 18 features. These results proved the advantage of the proposed method.en_US
dc.identifier.citationÖzyurt, F., Avcı, E., & Sert, E. (June 30, 2020). UC-Merced Image Classification with CNN Feature Reduction Using Wavelet Entropy Optimized with Genetic Algorithm. Traitement Du Signal, 37, 3, 347-353.en_US
dc.identifier.doi10.18280/ts.370301
dc.identifier.endpage353en_US
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85089302996en_US
dc.identifier.startpage347en_US
dc.identifier.urihttps://doi.org/10.18280/ts.370301
dc.identifier.urihttps://hdl.handle.net/20.500.12899/199
dc.identifier.volume37en_US
dc.identifier.wosWOS:000555439900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorSert, Eser
dc.language.isoenen_US
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCNNen_US
dc.subjectFeature reductionen_US
dc.subjectEntropyen_US
dc.subjectGenetic algorithmen_US
dc.subjectUC Merced dataseen_US
dc.subjectFeature-extractionen_US
dc.subjectNeural-networksen_US
dc.titleUC-merced image classification with CNN feature reduction using wavelet entropy optimized with genetic algorithmen_US
dc.typeArticleen_US

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