Multitask Learning of Vegetation Biochemistry from Hyperspectral Data
This work addresses a domain-specific challenge in remote sensing for vegetation analysis, offering an incremental improvement by enhancing prediction accuracy in data-scarce scenarios.
The paper tackles the problem of predicting vegetation biochemical contents from hyperspectral data when ground truth data is scarce for the target biochemical but available for related ones, by proposing a multitask Gaussian process method that transfers knowledge between tasks, resulting in outperformance over current methods on two real-world datasets.
Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex non-linear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets.