Name
Abstract | ||
|---|---|---|
Floods are among the most devastating natural hazards in the world, affecting more people and causing more property damage
than any other natural phenomena. One of the important problems associated with flood monitoring is a flood extent extraction
from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents
a new method to the flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations.
In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and
classification. We implemented our approach in a Grid system that was used to process data from three different satellite
sensors: ERS-2/SAR during the flooding on the river Tisza, Ukraine and Hungary (2001), ENVISAT/ASAR WSM (Wide Swath Mode)
and RADARSAT-1 during the flooding on the river Huaihe, China (2007). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s12145-008-0014-3 | Earth Science Informatics |
Keywords | Field | DocType |
flood extent.grid.neural networks. self-organisingkohonen maps.synthetic aperture radar,neural network,natural hazard,artificial neural network,synthetic aperture radar,self organization | Radar,Satellite,Satellite imagery,Synthetic aperture radar,Computer science,Remote sensing,Self-organizing map,Flooding (psychology),Natural hazard,Flood myth | Journal |
Volume | Issue | ISSN |
1 | 3-4 | 1865-0481 |
Citations | PageRank | References |
9 | 1.27 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nataliia Kussul | 1 | 191 | 25.01 |
| Andrii Shelestov | 2 | 145 | 19.39 |
| Serhiy Skakun | 3 | 47 | 6.38 |