Name
Abstract | ||
|---|---|---|
The orthogonal subspace projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel methods (KMs) makes the method nonlinear, helps to combat the high-dimensionality problem, and improves... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TGRS.2009.2020910 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Object detection,Kernel,Hyperspectral sensors,Hyperspectral imaging,Detectors,Matched filters,Remote sensing,Prototypes,Noise robustness,Crops | Kernel (linear algebra),Computer vision,Object detection,Subspace topology,Pattern recognition,Computer science,Incomplete Cholesky factorization,Robustness (computer science),Artificial intelligence,Kernel method,Nonlinear dimensionality reduction,Variable kernel density estimation | Journal |
Volume | Issue | ISSN |
47 | 11 | 0196-2892 |
Citations | PageRank | References |
8 | 0.61 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Capobianco | 1 | 90 | 6.20 |
| Andrea Garzelli | 2 | 574 | 41.36 |
| Gustavo Camps-Valls | 3 | 2011 | 114.02 |