Three-Dimensional Spatial-Spectral Filtering Based Feature Extraction for Hyperspectral Image Classification

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dc.contributor.authorAkyurek, Hasan Ali
dc.contributor.authorKocer, Baris
dc.date.accessioned2024-02-23T14:38:24Z
dc.date.available2024-02-23T14:38:24Z
dc.date.issued2017
dc.departmentNEÜen_US
dc.description.abstractHyperspectral pixels which have high spectral resolution are used to predict decomposition of material types on area of obtained image. Due to its multidimensional form, hyperspectral image classification is a challenging task. Hyperspectral images are also affected by radiometric noise. In order to improve the classification accuracy, many researchers are focusing on the improvement of filtering, feature extraction and classification methods. In the context of hyperspectral image classification, spatial information is as important as spectral information. In this study, a three-dimensional spatial-spectral filtering based feature extraction method is presented. It consists of three main steps. The first is a pre-processing step, which include spatial-spectral information filtering in three-dimensional space. The second comprises extract functional features of filtered data. The last one is combining extracted features by serial feature fusion strategy and using to classify hyperspectral image pixels. Experiments were conducted on two popular public hyperspectral remote sensing image, 1%, 5%, 10% and 15% of samples of each classes used as training set, the remaining is used as test set. The proposed method compared with well-known methods. Experimental results show that the proposed method achieved outstanding performance than compared methods in hyperspectral image classification task.en_US
dc.identifier.doi10.4316/AECE.2017.02013
dc.identifier.endpage102en_US
dc.identifier.issn1582-7445
dc.identifier.issn1844-7600
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85020078642en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage95en_US
dc.identifier.urihttps://doi.org/10.4316/AECE.2017.02013
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16514
dc.identifier.volume17en_US
dc.identifier.wosWOS:000405378100013en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUniv Suceava, Fac Electrical Engen_US
dc.relation.ispartofAdvances In Electrical And Computer Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaptive Algorithmsen_US
dc.subjectFeature Extractionen_US
dc.subjectGaussian Noiseen_US
dc.subjectHyperspectral Imagingen_US
dc.subjectImage Classificationen_US
dc.titleThree-Dimensional Spatial-Spectral Filtering Based Feature Extraction for Hyperspectral Image Classificationen_US
dc.typeArticleen_US

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