Kayacan, Yavuz EmreErer, Isin2025-03-092025-03-092024979-835036559-7https://doi.org/10.1109/TSP63128.2024.10605982https://hdl.handle.net/20.500.12662/491147th International Conference on Telecommunications and Signal Processing, TSP 2024 -- 10 July 2024 through 12 July 2024 -- Virtual, Online -- 201450The 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.eninfo:eu-repo/semantics/closedAccessautoencoderclutter removalGround Penetrating Radar (GPR)low-rank approximationnonnegative matrix factorization (NMF)Conference Object10.1109/TSP63128.2024.106059822-s2.0-85201159319335N/A332