Hyperspectral Image Classification Based on Multilayer Perceptron Trained with Eigenvalue Decay

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dc.authoridTOPUZ, VEDAT/0000-0001-7461-1849
dc.contributor.authorLokman, Gurcan
dc.contributor.authorCelik, Hasan Huseyin
dc.contributor.authorTopuz, Vedat
dc.date.accessioned2025-03-23T19:35:15Z
dc.date.available2025-03-23T19:35:15Z
dc.date.issued2020
dc.departmentSinop Üniversitesi
dc.description.abstractHyperspectral Images (HSI) require sufficient labeled samples and a complex classifier to identify an area. Support Vector Machine (SVM) is one of the most competent algorithms in this field. Neural Networks (NN) is another approach used for classification problems, and both have been widely proposed in the literature. The Convolutional Neural Network (CNN) method has also received significant attention in the deep learning field recently. Nevertheless, during NN training, the overfitting problem may cause continuous dragging of the algorithm toward larger error. In this case, a regularization technique is needed to constitute the most useful decision boundary. The Eigenvalue Decay method is one of the regularization techniques that may be applied for HSI. This study investigates the performance of Multilayer Perceptron trained with an Eigenvalue Decay (MLP-ED) algorithm for HSI classification. The SVM, CNN with Pixel-Pair and CNN-Ensemble methods are used as comparison algorithms for MLP-ED performance assessment. All methods were tested with 3 different high-resolution HSI datasets. While SVM is one of the classic classifiers, and the 2 new CNN algorithms show high performance, the proposed MLP-ED method has more computational efficiency and achieves higher success than the others do.
dc.identifier.doi10.1080/07038992.2020.1780572
dc.identifier.endpage271
dc.identifier.issn0703-8992
dc.identifier.issn1712-7971
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85087627346
dc.identifier.scopusqualityQ1
dc.identifier.startpage253
dc.identifier.urihttps://doi.org/10.1080/07038992.2020.1780572
dc.identifier.urihttps://hdl.handle.net/11486/5824
dc.identifier.volume46
dc.identifier.wosWOS:000547844500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofCanadian Journal of Remote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectNeural-Networks
dc.subjectTransform
dc.titleHyperspectral Image Classification Based on Multilayer Perceptron Trained with Eigenvalue Decay
dc.typeArticle

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