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Identification of buried objects based on peak scatter modelling of GPR A-scan signals
Author(s): Mehmet Sezgin; Burak Yoldemir; Ersin Özkan; Hakkı Nazlı
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Paper Abstract

A novel feature extraction and buried object identification method for ground penetrating radar data are presented. Discriminative features are obtained by modelling the most dynamic peaks of GPR A-scan signals, utilizing principal component analysis (PCA). Landmine/clutter discrimination is then achieved using fuzzy k-nearest neighbor algorithm. The identification results are presented on a real data set of 700 surrogate landmines and clutter objects, which were collected from three different terrains with various soil types and buried object depths. We show that the proposed method gives outstanding results over this extensive data set.

Paper Details

Date Published: 10 May 2019
PDF: 10 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101207 (10 May 2019); doi: 10.1117/12.2517691
Show Author Affiliations
Mehmet Sezgin, TÜBITAK BILGEM (Turkey)
Burak Yoldemir, TÜBITAK BILGEM (Turkey)
Ersin Özkan, TÜBITAK BILGEM (Turkey)
Hakkı Nazlı, TÜBITAK BILGEM (Turkey)


Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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