Title :
Automatic classification of GPR signals
Author :
Shao, W. ; Bouzerdoum, A. ; Phung, S.L. ; Su, L. ; Indraratna, B. ; Rujikiatkamjorn, C.
Author_Institution :
Sch. of Electr., Comput. & Telecommun. Eng., Univ. of Wollongong, Wollongong, NSW, Australia
Abstract :
Ground penetrating radar has been widely used in many areas. However, the processing and interpretation of acquired signals remains a challenging task since it requires experienced users to manage the whole operations. In this paper, we propose an automatic classification system to categorise GPR signals based on magnitude spectrum amplitudes and support vector machines. The system is tested on a real-world GPR data set. The experimental results show that our system can correctly distinguish ground penetrating radar signals reflected by different materials.
Keywords :
ground penetrating radar; radar signal processing; signal classification; support vector machines; GPR signal classification; ground penetrating radar; magnitude spectrum amplitudes; support vector machines; Electromagnetic scattering; Face detection; Filtering; Fourier transforms; Frequency; Ground penetrating radar; Humans; Low pass filters; Support vector machine classification; Support vector machines; GPR; SVM; classification; magnitude spectrum;
Conference_Titel :
Ground Penetrating Radar (GPR), 2010 13th International Conference on
Conference_Location :
Lecce
Print_ISBN :
978-1-4244-4604-9
Electronic_ISBN :
978-1-4244-4605-6
DOI :
10.1109/ICGPR.2010.5550187