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

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
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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).