Name
Abstract | ||
|---|---|---|
Mapping of surface water is useful in a variety of remote sensing applications, such as estimating the availability of water, measuring its change in time, and predicting droughts and floods. Using the imagery acquired by currently active Landsat missions, a surface water map can be generated from any selected region as often as every 8 days. Traditional Landsat water indices require carefully sel... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSTARS.2017.2735443 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Remote sensing,Earth,Satellites,Image segmentation,Water,Biological neural networks | Meteorology,Satellite,Ancillary data,Convolutional neural network,Surface water,Remote sensing,Terrain,Remote sensing application,Artificial intelligence,Deep learning,Mathematics,Snow | Journal |
Volume | Issue | ISSN |
10 | 11 | 1939-1404 |
Citations | PageRank | References |
4 | 0.54 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Furkan Isikdogan | 1 | 5 | 1.57 |
| Alan C. Bovik | 2 | 5062 | 349.55 |
| Paola Passalacqua | 3 | 10 | 2.73 |