Autoencoder Guided Low-Rank Approximation Approach for Clutter Removal in GPR Images

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dc.authorid0000-0002-2225-6379
dc.contributor.authorKayacan, Yavuz Emre
dc.contributor.authorErer, Isin
dc.date.accessioned2026-01-31T15:08:40Z
dc.date.available2026-01-31T15:08:40Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description47th International Conference on Telecommunications and Signal Processing-TSP-Annual -- JUL 10-12, 2024 -- CZECH REPUBLIC
dc.description.abstractThe performance of low-rank and sparse decomposition (LRSD) based clutter removal methods which are widely used in GPR systems depends heavily on the regularization parameter. This study proposes a. parameter-free low-rank approach. The low-rank component recovered by an autoencoder (AE) network is subtracted from the raw image to provide a clutter-free image. Simulation and experimental results validate the superiority of the proposed method compared to the low-rank approach Nonnegative Matrix Factorization (NMF) as well as other LRSD methods: Robust Principal Component Analysis (RPCA), Robust NMF (RNMF), and Robust Autoencoder (RAE).
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK) [120E234]
dc.description.sponsorshipInstitute of Electrical and Electronics Engineers Inc
dc.description.sponsorshipThis work was funded by the Scientific and Technological Research Council of Turkiye (TUBITAK) under Project No.120E234.
dc.identifier.doi10.1109/TSP63128.2024.10605982
dc.identifier.endpage335
dc.identifier.isbn9798350365603
dc.identifier.isbn9798350365597
dc.identifier.issn2835-009X
dc.identifier.startpage332
dc.identifier.urihttps://doi.org./10.1109/TSP63128.2024.10605982
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10724
dc.identifier.wosWOS:001594113800068
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof2024 47Th International Conference on Telecommunications And Signal Processing, Tsp 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260128
dc.subjectGround Penetrating Radar (GPR)
dc.subjectclutter removal
dc.subjectlow-rank approximation
dc.subjectautoencoder
dc.subjectnonnegative matrix factorization (NMF)
dc.titleAutoencoder Guided Low-Rank Approximation Approach for Clutter Removal in GPR Images
dc.typeConference Object

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