Name
Abstract | ||
|---|---|---|
Hybrid classification methods based on consensus from several data sources are considered. Each data source is at first treated separately and modeled using statistical methods. Then weighting mechanisms are used to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and nonlinear optimization methods are considered and used in classification of two multisource remote sensing and geographic data sets. A nonlinear method which utilizes a neural network gives excellent experimental results. The hybrid statistical/neural method outperforms all other methods in terms of test accuracies in the experiments. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/36.602526 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
geophysical signal processing,geophysical techniques,geophysics computing,image classification,neural nets,remote sensing,sensor fusion,combined classification,geographic data,geophysical measurement technique,hybrid consensus theoretic classification,image processing,land surface,neural net,neural network,nonlinear optimization method,statistical method,terrain mapping,weighting mechanism,testing,fuzzy logic,data mining,statistical analysis,neural networks,nonlinear optimization | Data mining,Weighting,Data set,Nonlinear system,Computer science,Fuzzy logic,Remote sensing,Nonlinear programming,Sensor fusion,Contextual image classification,Artificial neural network | Journal |
Volume | Issue | ISSN |
35 | 4 | 0196-2892 |
Citations | PageRank | References |
28 | 4.30 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. A. Benediktsson | 1 | 860 | 83.81 |
| Johannes R. Sveinsson | 2 | 1150 | 95.58 |
| P. H. Swain | 3 | 327 | 77.70 |