Name
Abstract | ||
|---|---|---|
This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information. This framework of composite kernels demonstrates: 1) enhanced classification accuracy as compared to traditional approaches th... |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/LGRS.2005.857031 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
Kernel,Hyperspectral imaging,Image classification,Hyperspectral sensors,Support vector machines,Support vector machine classification,Robustness,Neural networks,Computational efficiency | Journal | 3 |
Issue | ISSN | Citations |
1 | 1545-598X | 112 |
PageRank | References | Authors |
6.62 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camps-Valls, G. | 1 | 441 | 29.69 |
| L. G'omez-Chova | 2 | 181 | 13.79 |
| Jordi Muñoz-Marí | 3 | 559 | 40.11 |
| J. Vila-Franc'es | 4 | 112 | 6.62 |
| javier calpemaravilla | 5 | 115 | 7.66 |