Name
Abstract | ||
|---|---|---|
The classification of hyperspectral imagery, using multiple classifier systems is discussed and an SVM-based ensemble is introduced. The data set is separated into separate feature subsets using the correlation between the different spectral bands as a criterion. Afterwards, each source is classified separately by an SVM classifier. Finally, the different outputs are used as inputs for final decision fusion that is based on an additional SVM classifier. The results using the proposed strategy are compared to classification results achieved by a single SVM and other well known classifier ensembles, such as random forests, boosting and bagging. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02326-2_7 | MCS |
Keywords | Field | DocType |
classifier ensemble,svm classifier,additional svm classifier,classifying hyperspectral remote sensing,single svm,multiple classifier system,ensemble strategies,svm-based ensemble,classification result,different output,different spectral band,random forest,hyperspectral imagery,support vector | Structured support vector machine,Data mining,Pattern recognition,Computer science,Support vector machine,Hyperspectral imaging,Artificial intelligence,Boosting (machine learning),Classifier (linguistics),Margin classifier,Random forest,Quadratic classifier | Conference |
Volume | ISSN | Citations |
5519 | 0302-9743 | 5 |
PageRank | References | Authors |
0.39 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xavier Ceamanos | 1 | 34 | 8.54 |
| Björn Waske | 2 | 435 | 24.75 |
| JÓn Atli Benediktsson | 3 | 635 | 28.85 |
| Jocelyn Chanussot | 4 | 4145 | 272.11 |
| Johannes R. Sveinsson | 5 | 1150 | 95.58 |