Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows

TL;DR

This paper introduces a Kernel Flows-based approach to optimize kernel parameters in hyperspectral image data analysis, improving regression models like K-PCR and KF-PLS, and benchmarks them against other nonlinear methods.

Contribution

It proposes a novel KF-type method for optimizing Kernel Principal Component Regression, extending previous KF applications to new hyperspectral data analysis models.

Findings

KF-optimized K-PCR and KF-PLS outperform traditional methods

Kernel Flows effectively reduce overfitting in hyperspectral regression

Benchmarking shows competitive performance against nonlinear techniques

Abstract

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.