Name
Title | ||
|---|---|---|
An electromagnetic approach based on neural networks for the GPR investigation of buried cylinders |
Abstract | ||
|---|---|---|
In this letter, neural networks (NNs) are used to reconstruct the geometric and dielectric characteristics of buried cylinders. The NN is designed to work with input data extracted from the transient electric fields scattered by the target. To this aim, a simple simulation of a typical ground-penetrating radar setting is performed and different sets of data examined. Moreover, different neural network algorithms have been exploited, and results have been compared. Finally, the "robustness" of the proposed approach has been tested against noisy data and against uncertainties in the modelization. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/LGRS.2004.839648 | Geoscience and Remote Sensing Letters, IEEE |
Keywords | Field | DocType |
buried object detection,electromagnetic wave scattering,geophysical techniques,ground penetrating radar,neural nets,remote sensing by radar,GPR investigation,buried cylinders,dielectric characteristics,electromagnetic method,geometric characteristics,ground-penetrating radar setting simulation,modelization uncertainty,neural network algorithms,noisy data,transient electric fields | Radar engineering details,Radar,Noisy data,Cylinder (engine),Ground-penetrating radar,Remote sensing,Electromagnetic wave scattering,Robustness (computer science),Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
2 | 1 | 1545-598X |
Citations | PageRank | References |
9 | 0.81 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvatore Caorsi | 1 | 40 | 8.65 |
| Cevini, G. | 2 | 9 | 0.81 |