Identification Of Buried Objects Based On Peak Scatter Modelling Of Gpr A-Scan Signals

Autor:	Hakki Nazli, Ersin Özkan, Mehmet Sezgin, Burak Yoldemir
Rok vydání:	2019
Předmět:	Data set Set (abstract data type) Identification (information) Discriminative model Computer science business.industry Ground-penetrating radar Feature extraction Principal component analysis Clutter Pattern recognition Artificial intelligence business
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64dbda2c494525e91e6b25ced2c30e72 https://aperta.ulakbim.gov.tr/record/100211 Zobrazit plný text záznamu