Name
Abstract | ||
|---|---|---|
We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of each feature after optimization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5417372 | Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009 |
Keywords | Field | DocType |
Gaussian processes,support vector machines,vegetation mapping,Bayesian learning,Kernel method,adaptive Gaussian processes,biophysical parameter estimation,chlorophyll concentration,leaf membrane permeability,noise variance,nonparametric model,support vector regression,Bayesian learning,Biophysical parameter estimation,Gaussian Process,Kernel method,Support Vector Regression,chlorophyll concentration,leaf membrane permeability,non-parametric model | Bayesian inference,Artificial intelligence,Gaussian process,Estimation theory,Kernel (linear algebra),Computer vision,Pattern recognition,Hyperparameter,Support vector machine,Algorithm,Marginal likelihood,Kernel method,Mathematics | Conference |
Volume | ISBN | Citations |
4 | 978-1-4244-3395-7 | 5 |
PageRank | References | Authors |
0.68 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camps-Valls, G. | 1 | 5 | 0.68 |
| Gomez-Chova, L. | 2 | 12 | 2.32 |
| Muoz-Mari, J. | 3 | 5 | 0.68 |
| Vila-Frances, J. | 4 | 5 | 0.68 |