English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ground-penetrating radar (GPR) has been widely used to detect subsurface objects. However, the target reflection experiences interference owing to clutter, such as direct coupling, interface reflection, and other undesired reflections. Reducing such clutter is a valuable processing step to improve detection accuracy. This letter proposes a GPR clutter reduction algorithm based on dictionary learning (DL). DL learns an adaptive dictionary that can sparsely represent the GPR B-scan image and enable clutter suppression. The learned dictionary atoms are divided into target atoms and clutter atoms, the target component and the clutter component can be reconstructed using these two types of dictionary atoms, respectively. The performance of the proposed algorithm is validated using both simulation data and real GPR data. The results of the visual evaluation and quantitative analysis demonstrate that the proposed algorithm outperforms the existing clutter reduction algorithms.

Using dictionary learning for clutter reduction in GPR B-scan images