The curvelet transform application to the analysis of data received from GPR technique
Artykuł w czasopiśmie
MNiSW
15
Lista A
| Status: | |
| Autorzy: | Cięszczyk Sławomir, Ławicki Tomasz, Miaskowski Arkadiusz |
| Rok wydania: | 2013 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 6 |
| Wolumen/Tom: | 19 |
| Strony: | 99 - 102 |
| Impact Factor: | 0,445 |
| Web of Science® Times Cited: | 19 |
| Scopus® Cytowania: | 22 |
| Bazy: | Web of Science | Scopus | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Abstrakty: | angielski |
| The article presents the analysis of both, synthetic and experimental Ground Penetrating Radar (GPR) data by the use of curvelet transform. The images, received from GPR technique, called B-scans are 2D images, where the first dimension represents time and the other one represents depth. These images are often low resolution and noisy. Besides, Bscans are often very difficult to interpret even for experts. That is why the curvelet transform has been applied to clarify the meaning of images. We have shown that the image analysis process of B-scans can be automated by the use of curvelet transform, which lets us detect the edges in any angle in the image under consideration. It was proven that the proper analysis and the choice of curvelet transform coefficients allows to clearly specify the location of the target. The main advantage of the curvelet analysis is the efficient detection even when we deal with low resolution and noisy images. |
