6533b7d2fe1ef96bd125dfc6
RESEARCH PRODUCT
Sensitivity analysis of Gaussian processes for oceanic chlorophyll prediction
Katalin BlixRobert JenssenGustau Camps-vallssubject
symbols.namesakeKrigingGround-penetrating radarsymbolsProbabilistic logicFeature (machine learning)Kernel regressionSpectral bandsSensitivity (control systems)Biological systemGaussian processRemote sensingMathematicsdescription
Gaussian Process Regression (GPR) for machine learning has lately been successfully introduced for chlorophyll content mapping from remotely sensed data. The method provides a fast, stable and accurate prediction of biophysical parameters. However, since GPR is a non-linear kernel regression method, the relevance of the features are not accessible. In this paper, we introduce a probabilistic approach for feature sensitivity analysis (SA) of the GPR in order to reveal the relative importance of the features (bands) being used in the regression process. We evaluated the SA on GPR ocean chlorophyll content prediction. The method revealed the importance of the spectral bands, thus allowing the discrimination between Case-1 water and Case-2 water conditions.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-07-01 | 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |