Name
Abstract | ||
|---|---|---|
Neural networks have the ability to represent and learn complex regression functions and are very suitable for retrieval of geophysical parameters from remotely sensed data. Neural networks trained to minimize the mean square error are able to estimate the conditional expectation of target variables. In many remote sensing applications, it is also critical to provide estimates of prediction uncert... |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TGRS.2011.2166120 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Uncertainty,Accuracy,MODIS,Estimation,Noise,Aerosols,Training | Satellite,Regression analysis,Remote sensing,Mean squared error,Conditional expectation,Remote sensing application,Uncertainty analysis,Artificial neural network,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
50 | 2 | 0196-2892 |
Citations | PageRank | References |
6 | 0.55 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kosta Ristovski | 1 | 52 | 5.12 |
| Slobodan Vucetic | 2 | 637 | 56.38 |
| Zoran Obradovic | 3 | 1110 | 137.41 |