Name
Abstract | ||
|---|---|---|
Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14195038 | REMOTE SENSING |
Keywords | DocType | Volume |
ground-penetrating radar, non-destructive-evaluation, deep learning | Journal | 14 |
Issue | ISSN | Citations |
19 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ildar Gilmutdinov | 1 | 0 | 0.34 |
| Ingrid Schloegel | 2 | 0 | 0.34 |
| Alois Hinterleitner | 3 | 0 | 0.34 |
| Peter Wonka | 4 | 2854 | 165.59 |
| Michael Wimmer | 5 | 1279 | 81.45 |