Improved Clutter Removal in GPR by Robust Nonnegative Matrix Factorization

Kumlu D., Erer I.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol.17, no.6, pp.958-962, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 6
  • Publication Date: 2020
  • Doi Number: 10.1109/lgrs.2019.2937749
  • Journal Name: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.958-962
  • Keywords: Clutter, Matrix decomposition, Ground penetrating radar, Sparse matrices, Principal component analysis, Antenna accessories, Clutter removal, ground-penetrating radar (GPR), low-rank and sparse matrix decomposition, nonnegative matrix decomposition, LOW-RANK, SPARSE
  • Istanbul Technical University Affiliated: Yes

Abstract

The clutter encountered in the ground-penetrating radar (GPR) system severely decreases the visibility of subsurface objects, thus highly degrading the performance of the target detection algorithms. This letter presents a new clutter removal method based on nonnegative matrix factorization (NMF). The raw GPR data are represented as the sum of low-rank and sparse matrices, which correspond to the clutter and target components, respectively. The low-rank and sparse decomposition is performed using a robust version of NMF called RNMF. Although similar to the robust principal component analysis (PCA) (RPCA), which is recently widely used in image processing applications as well as in GPR, the proposed method is faster and has enhanced results. The state-of-the-art clutter removal methods, morphological component analysis (MCA), RPCA, besides the conventional PCA, have been included for comparison for both simulated and real data sets. The visual and quantitative results demonstrate that the proposed RNMF method outperforms the others. Moreover, it is 25 times faster than the RPCA for the given regularization parameter values.