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Sensitivity analysis of Gaussian process machine learning for chlorophyll prediction from optical remote sensing
(Master thesis; Mastergradsoppgave, 2014-05-30)
The machine learning method, Gaussian Process Regression (GPR), has lately been introduced for chlorophyll content mapping from remotely sensed data. It has been shown that GPR has outperformed other machine learning and empirical methods in accuracy, speed and stability. Moreover, GPR not only estimates the chlorophyll content, it also provides the certainty level of the prediction, allowing the ...