Name
Abstract | ||
|---|---|---|
The main research goal of this study is to investigate the complementarity and fusion of different frequencies (L- and P-band), polarimetric SAR (PolSAR) and polarimetric interferometric (PolInSAR) data for land cover classification. A large feature set was derived from each of these four modalities and a two-level fusion method was developed: Logistic regression (LR) as ‘feature-level fusion’ and the neural-network (NN) method for higher level fusion. For comparison, a support vector machine (SVM) was also applied. NN and SVM were applied on various combinations of the feature sets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.jag.2009.01.004 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
PolSAR,PolInSAR,Fusion,Neural network architecture,Land cover classification,Feature extraction | Polarimetry,Pattern recognition,Regression,Synthetic aperture radar,Remote sensing,Support vector machine,Fusion,Feature extraction,Artificial intelligence,Artificial neural network,Land cover,Geography | Journal |
Volume | Issue | ISSN |
11 | 3 | 0303-2434 |
Citations | PageRank | References |
21 | 0.93 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Shimoni | 1 | 21 | 0.93 |
| Dirk Borghys | 2 | 43 | 6.07 |
| R. Heremans | 3 | 21 | 0.93 |
| C. Perneel | 4 | 45 | 4.55 |
| M Acheroy | 5 | 112 | 9.37 |