GPR Clutter Reduction by Robust Orthonormal Subspace Learning

Kumlu D., Erer I.

IEEE ACCESS, cilt.8, ss.74145-74156, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1109/access.2020.2988333
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.74145-74156
  • Anahtar Kelimeler: Ground penetrating radar, Clutter, Sparse matrices, Robustness, Minimization, Matrix decomposition, Principal component analysis, Clutter reduction, low rank and sparse matrix decomposition, robust subspace learning, GPR, GROUND-PENETRATING RADAR, REMOVAL
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

The clutter severely decreases the target visibility, thus the detection rates in ground penetrating radar (GPR) systems. Recently proposed robust principal component analysis (RPCA) based clutter removal method decomposes the GPR image into its low rank and sparse parts corresponding to clutter and target components. Motivated by its encouraging results, many lower complexity low rank and sparse decomposition (LRSD) methods such as go decomposition (GoDec) or robust non-negative matrix factorization (RNMF) have been applied to GPR. This paper proposes a new clutter reduction method using robust orthonormal subspace learning (ROSL). The raw GPR image is decomposed into its clutter and target parts via ROSL. The proposed method is faster than the popular RPCA. Although it has similar complexity, and similar performance with GoDec and RNMF for fine tuned parameters of these methods, the proposed method does not require any presetting of the algorithm parameters. Its performance remains independent for a broad range parameter value. Results demonstrate that the proposed method achieves 14 & x2013; 48 & x0025; higher performance in terms of PSNR values than the state-of-the-art LRSD methods for an arbitrary parameter choice.