Cross-validating Gaussian process methods for hyperspectral data from tree crowns

摘要

We propose LOCO-CV-GP, a method for cross-validating Gaussian process (GP) methods in a leave-one-crown-out (LOCO) manner, when the GP method is applied on hyperspectral data from tree crowns. The fact that spectra within a crown are correlated [1] needs to be taken into consideration when working with airborne HS tree spectra. The experiments are conducted on OSBS2014 dataset to cross-validate OGP, a soft one-class classification method based on GP [2, 3]. Specifically, LOCO is justified by comparing LOCO-CV-GP to CV-GP where signals within a crown is split between cross-validation (CV) folds. Even though the proposed method was motivated by HS tree classification, it can be applied to any data where signal correlation happen within known partitions of the training signals.