The multiscale directional neighborhood filter and its application to clutter removal in GPR data

Kumlu D., Erer I.

SIGNAL IMAGE AND VIDEO PROCESSING, cilt.12, sa.7, ss.1237-1244, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 7
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s11760-018-1275-z
  • Dergi Adı: SIGNAL IMAGE AND VIDEO PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1237-1244
  • Anahtar Kelimeler: Clutter removal, Image decomposition, Directional filter bank, Neighborhood filtering, Multiscale transform, Ground-penetrating radar, GROUND-PENETRATING RADAR, IMAGES
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

We present a novel neighborhood filter (NF)-based clutter removal algorithm in ground-penetrating radar (GPR) images. Since NF uses only range kernel of the well-known bilateral filter, it is less complex and makes clutter removal method appropriate for real-time implementations. We extend NF to multiscale-multidirectional case: MDNF and then decompose the GPR image into approximation and detail subbands to capture the intrinsic geometrical structures that contain both target and clutter information. After directional decomposition, the clutter is eliminated by keeping the diagonal information for target component. Finally, the inverse transform is applied to the remaining subbands for reconstruction of clutter-free GPR image. Results of both simulated and real datasets validate the superiority of MDNF over the state-of-the-art methods, and it improves in the false alarm rate further by 5.5% at maximum detection performance.

We present a novel neighborhood filter (NF)-based clutter removal algorithm in ground-penetrating radar (GPR) images.

Since NF uses only range kernel of the well-known bilateral filter, it is less complex and makes clutter removal method

appropriate for real-time implementations. We extend NF to multiscale–multidirectional case: MDNF and then decompose

the GPR image into approximation and detail subbands to capture the intrinsic geometrical structures that contain both target

and clutter information. After directional decomposition, the clutter is eliminated by keeping the diagonal information for

target component. Finally, the inverse transform is applied to the remaining subbands for reconstruction of clutter-free GPR

image. Results of both simulated and real datasets validate the superiority of MDNF over the state-of-the-art methods, and it

improves in the false alarm rate further by 5.5% at maximum detection performance.