Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
This work addresses the optimization of kernel parameters for chemometric methods in hyperspectral remote sensing, which is incremental as it extends existing Kernel Flows to a new model.
The authors tackled the problem of optimizing kernel parameters for kernel-based retrieval models in hyperspectral image data by proposing a new Kernel Flows approach for Kernel Principal Component Regression (K-PCR) and testing it alongside KF-PLS, benchmarking against non-linear regression techniques on two datasets.
Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based chemometric methods is limited and often reduced to grid searching. Previously, the authors introduced Kernel Flows (KF) to learn kernel parameters for Kernel Partial Least-Squares (K-PLS) regression. KF is easy to implement and helps minimize overfitting. In cases of high collinearity between spectra and biogeophysical quantities in spectroscopy, simpler methods like Principal Component Regression (PCR) may be more suitable. In this study, we propose a new KF-type approach to optimize Kernel Principal Component Regression (K-PCR) and test it alongside KF-PLS. Both methods are benchmarked against non-linear regression techniques using two hyperspectral remote sensing datasets.