Identification of buried objects based on peak scatter modelling of GPR A-scan signals

Mehmet Sezgin; Burak Yoldemir; Ersin Özkan; Hakkı Nazlı

doi:10.1117/12.2517691

10 May 2019 Identification of buried objects based on peak scatter modelling of GPR A-scan signals

Mehmet Sezgin, Burak Yoldemir, Ersin Özkan, Hakkı Nazlı

Proceedings Volume 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV; 1101207 (2019) https://doi.org/10.1117/12.2517691
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Mehmet Sezgin, Burak Yoldemir, Ersin Özkan, and Hakkı Nazlı "Identification of buried objects based on peak scatter modelling of GPR A-scan signals", Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101207 (10 May 2019); https://doi.org/10.1117/12.2517691

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available