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Sparse representation of GPR traces with application to signal classification

Journal Article


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Abstract


  • Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for pattern classification. © 1980-2012 IEEE.

Publication Date


  • 2013

Citation


  • W. Shao, A. Bouzerdoum & S. Lam. Phung, "Sparse representation of GPR traces with application to signal classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 51, (7) pp. 3922-3930, 2013.

Scopus Eid


  • 2-s2.0-84880284941

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2169&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1160

Has Global Citation Frequency


Number Of Pages


  • 8

Start Page


  • 3922

End Page


  • 3930

Volume


  • 51

Issue


  • 7

Place Of Publication


  • United States

Abstract


  • Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for pattern classification. © 1980-2012 IEEE.

Publication Date


  • 2013

Citation


  • W. Shao, A. Bouzerdoum & S. Lam. Phung, "Sparse representation of GPR traces with application to signal classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 51, (7) pp. 3922-3930, 2013.

Scopus Eid


  • 2-s2.0-84880284941

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2169&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1160

Has Global Citation Frequency


Number Of Pages


  • 8

Start Page


  • 3922

End Page


  • 3930

Volume


  • 51

Issue


  • 7

Place Of Publication


  • United States