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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenbin Shao Bouzerdoum, A. Son Lam Phung |
| Copyright Year | 1980 |
| 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. |
| Sponsorship | IEEE Geoscience and Remote Sensing Society IEEE URSI |
| Starting Page | 3922 |
| Ending Page | 3930 |
| Page Count | 9 |
| File Size | 749897 |
| File Format | |
| ISSN | 01962892 |
| Volume Number | 51 |
| Issue Number | 7 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ground penetrating radar Dictionaries Signal resolution Rail transportation Matching pursuit algorithms Feature extraction Electronic ballasts sparse representation (SR) Ground penetrating radar (GPR) pattern classification signal decomposition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Electrical and Electronic Engineering |
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