Sparse Representation of GPR Traces With Application to Signal Classification

作者：Shao Wenbin^*; Bouzerdoum Abdesselam; Phung Son Lam

来源：IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(7): 3922-3930.

DOI：10.1109/TGRS.2012.2228660

• Ground penetrating radar (GPR)
• pattern classification
• signal decomposition
• sparse representation (SR)

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.

• 出版日期2013-7