An efficient feature extraction approach for hyperspectral images using wavelet high dimensional model representation

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dc.authoridMehmet Alper Tunga / 0000-0003-3551-4549en_US
dc.authorscopusidMehmet Alper Tunga / 8555922400en_US
dc.authorwosidMehmet Alper Tunga / N-9306-2013en_US
dc.contributor.authorTuna, Süha
dc.contributor.authorÖzay, Evrim Korkmaz
dc.contributor.authorTunga, Burcu
dc.contributor.authorGürvit, Ercan
dc.contributor.authorTunga, Mehmet Alper
dc.date.accessioned2023-01-26T12:45:55Z
dc.date.available2023-01-26T12:45:55Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
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.en_US
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.en_US
dc.identifier.doi10.1080/01431161.2022.2147036en_US
dc.identifier.endpage6920en_US
dc.identifier.issn0143-1161en_US
dc.identifier.issn1366-5901en_US
dc.identifier.issue19-24en_US
dc.identifier.startpage6899en_US
dc.identifier.urihttp://dx.doi.org/10.1080/01431161.2022.2147036
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3846
dc.identifier.volume43en_US
dc.identifier.wosWOS:000897079200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorTunga, Mehmet Alper
dc.language.isoenen_US
dc.publisherTAYLOR & FRANCIS LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF REMOTE SENSINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral Dataen_US
dc.subjectFeature Extractionen_US
dc.subjectClassificationen_US
dc.subjectWaveletsen_US
dc.subjectHigh Dimensional Model Representationen_US
dc.titleAn efficient feature extraction approach for hyperspectral images using wavelet high dimensional model representationen_US
dc.typeArticleen_US

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