Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods

Basit Öğe Kaydı
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.contributor.authorÖzdemir, Akın
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.date.accessioned2023-06-01T06:21:51Z
dc.date.available2023-06-01T06:21:51Z
dc.date.issued2021en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.en_US
dc.description.abstractHyperspectral imaging (HSI) is one of the most advanced methods of digital imaging. This technique differs from RGB images with its wide range of the electromagnetic spectrum. Imbalanced data sets are frequently encountered in machine learning. As a result, the classifier performance may be poor. To avoid this problem, the data set must be balanced. The main motivation in this study is to reveal the difference and effects on the classifier performance between the original imbalanced dataset and the data set modified by balancing methods. In the proposed method, hyperspectral image classification study carried out on Xuzhou Hyspex dataset includes nineclasses including bareland-1, bareland-2, crops-1, crops-2, lake, coals, cement, trees, house-roofs of elements, by using the convolutional neural networks (CNN) and dataset balancing methods comprising the Smote, Adasyn, KMeans, and Cluster. This dataset has been taken from IEEE-Dataport Machine Learning Repository. To classify the hyperspectral image, the convolutional neural networks having different multiclass classification approaches like One-vs-All, One-vs-One. Dataset was splitted in two different ways: %50-%50 Hold-out and 5-Fold Crossvalidation. In order to evaluate the performance of the proposed models, the confusion matrix, classification accuracy, precision, recall, and F-Measure have been used. Without the dataset balancing, the obtained classification accuracies are 93.63%, 92.33%, 88.36% for %50-%50 train-test split, and 94.46%, 94%, 92.24% for 5Fold cross-validation using multi-class classification, One-vs-All, and One-vs-One respectively. After Smote balancing, the obtained classification accuracies are 96.41%, 95.6%, 92.53% for %50-%50 train-test split and 96.49%, 95.64%, 93.38% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. After Adasyn balancing, the obtained classification accuracies are 95.86%, 93.62%, 87.05% for % 50-%50 train-test split and 96.38%, 95.09%, 91.55% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. After K-Means balancing, the obtained classification accuracies are 95.23%, 93.36%, 90.6% for %50-%50 train-test split and 95.74%, 94.72%, 91.94% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. After Cluster balancing, the obtained classification accuracies are 94.83%, 94.1%, 90.07% for %50-%50 train-test split and 96.28%, 95.88%, 92.5% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. The obtained results have shown that the best model is Smote Balanced 5-CV multiclass classification.en_US
dc.description.sponsorshipDeanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabiaen_US
dc.identifier.citationÖzdemir, A., Polat, K., & Alhudhaif, A. (2021). Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods. Expert Systems with Applications, 178, 114986.en_US
dc.identifier.doi10.1016/j.eswa.2021.114986
dc.identifier.endpage44en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85104731922en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2021.114986
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11038
dc.identifier.volume178en_US
dc.identifier.wosWOS:000696678400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖzdemir, Akın
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral Imagingen_US
dc.subjectDeep Learningen_US
dc.subjectImage Classificationen_US
dc.subjectSMOTEen_US
dc.subjectImbalanced Dataseten_US
dc.subjectImbalanced Classificationen_US
dc.titleClassification of imbalanced hyperspectral images using SMOTE-based deep learning methodsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
akin-ozdemir.pdf
Boyut:
51.56 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin/Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu