Name
Abstract | ||
|---|---|---|
We present a novel inversion approach using a neural network to locate subsurface targets and evaluate their backscattering properties from ground penetrating radar (GPR) data. The presented inversion strategy constructs an adaptive linear element (ADALINE) neural network, whose configuration is related to the unknown properties of the targets. The GPR data is reconstructed (compression) to fit the structure of the neural network. The constructed neural network works with a supervised training mode, where a series of primary functions derived from the GPR signal model are used as the input, and the reconstructed GPR data is the expected/target output. In this way, inverting the GPR data is the equivalent of training the network. The back-propagation (BP) algorithm is employed for the training of the neural network. The numerical experiments show that the proposed approach can return an exact estimation for the target's location. Under sparse conditions, an inverted backscattering intensity with a relative error lower than 3% was achieved, whereas for the multi-dominating point scenario, a higher error rate was observed. Finally, the limitations and further developments for the inverting GPR data with the neural network are discussed. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/rs10050730 | REMOTE SENSING |
Keywords | Field | DocType |
ground penetrating radar,neural network,adaptive linear element,inversion | Computer vision,Ground-penetrating radar,Inversion (meteorology),Word error rate,Backscatter,Algorithm,Artificial intelligence,Supervised training,Geology,Artificial neural network,Approximation error | Journal |
Volume | Issue | ISSN |
10 | 5 | 2072-4292 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Liu | 1 | 3 | 3.16 |
| Yi Su | 2 | 172 | 29.50 |
| Chunlin Huang | 3 | 36 | 7.22 |