An efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representation

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dc.authorscopusidMehmet Alper Tunga / 8555922400
dc.authorwosidMehmet Alper Tunga / IHU-2933-2023
dc.contributor.authorTuna, Süha
dc.contributor.authorKorkmaz Özay, Evrim
dc.contributor.authorTunga, Burcu
dc.contributor.authorGürvit, Ercan
dc.contributor.authorTunga, Mehmet Alper
dc.date.accessioned2025-04-18T07:40:00Z
dc.date.available2025-04-18T07:40:00Z
dc.date.issued2022
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractHyperspectral (HS) Imagery helps to capture information using specialized sensors to extract detailed data at numerous narrow wavelengths. Hyperspectral imaging provides both spatial and spectral characteristics of regions or objects for subsequent analysis. Unfortunately, various noise sources decrease the interpretability of these images as well as the correlation between neighbouring pixels, hence both reduce the classification performance. This study focuses on developing an ensemble algorithm that enables to denoise the spectral signals while decorrelating the spatio-spectral features concurrently. The developed method is called Wavelet High Dimensional Model (W-HDMR) and combines High Dimensional Model Representation (HDMR) with the Discrete Wavelet Transform (DWT). Through W-HDMR, denoised and decorrelated features are extracted from the HS cubes. HDMR decorrelates each dimension in HS data while DWT denoises the spectral signals. The classification performance of W-HDMR as a new feature extraction technique for HS images is assessed by exploiting a Support Vector Machines algorithm. The results indicate that the proposed W-HDMR method is an efficient feature extraction technique and is considered an adequate tool in the HS classification problem.
dc.description.sponsorshipİstanbul Teknik Üniversitesi, Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi -2021-43503
dc.identifier.citationTuna, S., Korkmaz Özay, E., Tunga, B., Gürvit, E., & Tunga, M. A. (2022). An efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representation. International Journal of Remote Sensing, 43(19-24), 6899-6920.
dc.identifier.doi10.1080/01431161.2022.2147036
dc.identifier.endpage6920
dc.identifier.issn01431161
dc.identifier.issue19-24
dc.identifier.scopus2-s2.0-85146921688
dc.identifier.scopusqualityQ1
dc.identifier.startpage6899
dc.identifier.urihttp://dx.doi.org/10.1080/01431161.2022.2147036
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6473
dc.identifier.volume43
dc.identifier.wosWOS:000897079200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.institutionauthorTunga, Mehmet Alper
dc.institutionauthoridMehmet Alper Tunga / 0000-0003-3551-4549
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.ispartofInternational Journal of Remote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHyperspectral Data
dc.subjectFeature Extraction
dc.subjectClassification
dc.subjectWavelets
dc.subjectHigh Dimensional Model Representation
dc.titleAn efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representation
dc.typeArticle

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