Name
Title | ||
|---|---|---|
A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks. |
Abstract | ||
|---|---|---|
Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2017.2772349 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Synthetic aperture radar,Tiles,Tsunami,Image recognition,Training,Neural networks | Residual,Computer vision,Synthetic aperture radar,Selection algorithm,Emergency management,Artificial intelligence,Recognition algorithm,Deep learning,Artificial neural network,Mathematics,Deep neural networks | Journal |
Volume | Issue | ISSN |
15 | 1 | 1545-598X |
Citations | PageRank | References |
5 | 0.51 | 13 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanbing Bai | 1 | 5 | 0.51 |
| Chang Gao | 2 | 23 | 1.18 |
| Sameer Singh | 3 | 1060 | 71.63 |
| Magaly Koch | 4 | 36 | 12.93 |
| Bruno Adriano | 5 | 9 | 3.68 |
| Erick Mas | 6 | 10 | 4.36 |
| Shunichi Koshimura | 7 | 25 | 11.10 |