GPR clutter reduction by multi-resolution based tensor RPCA


Kumlu D., Erer I.

INTERNATIONAL JOURNAL OF REMOTE SENSING, vol.42, no.19, pp.7295-7312, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 19
  • Publication Date: 2021
  • Doi Number: 10.1080/01431161.2021.1956700
  • Title of Journal : INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Page Numbers: pp.7295-7312

Abstract

A new clutter reduction method which utilizes the multi-resolution and multi-directional information of the ground-penetrating radar (GPR) image is proposed. Sub-images obtained by stationary wavelet transform (SWT) or nonsubsampled counterlet transform (NSCT) are cast into a tensor structure presenting higher information compared to the spatial input data. A tensor-robust principal component analysis (TRPCA) algorithm is used for low-rank and sparse decomposition (LRSD) followed by inverse transform of the sparse tensor component to provide the clutter reduction results. The proposed methods TRPCA-SWT and TRPCA-NSCT are compared both visually and quantitatively to robust principal component analysis (RPCA) and TRPCA-bandpass filter (TRPCA-BPF), which employ the spatial raw GPR data and outputs of simple low-pass and high-pass filters respectively. Visual and quantitative results demonstrate that the clutter reduction performance increases when a higher number of scales and directions are used prior to the LRSD decomposition. Moreover, one of the proposed methods, TRPCA-NSCT, removes the background noise more efficiently due to its higher multi-resolution and multi-direction investigation capability, increasing the performance of the target detection algorithms.