Autoencoder Guided Low-Rank Approximation Approach for Clutter Removal in GPR Images
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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 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). © 2024 IEEE.
Açıklama
47th International Conference on Telecommunications and Signal Processing, TSP 2024 -- 10 July 2024 through 12 July 2024 -- Virtual, Online -- 201450
Anahtar Kelimeler
autoencoder, clutter removal, Ground Penetrating Radar (GPR), low-rank approximation, nonnegative matrix factorization (NMF)
Kaynak
2024 47th International Conference on Telecommunications and Signal Processing, TSP 2024
WoS Q Değeri
Scopus Q Değeri
N/A
