Abstract:
Hyperspectral sensors capture and compute spectral reflectance of objects over many
wavelength bands, resulting in a high-dimensional space with enough information to differentiate
between spectrally similar objects. Due to the curse of dimensionality, high spectral dimensionality
can also be difficult to handle and analyse, demanding complex processing and the use of advanced
analytical techniques. Moreover, when hyperspectral measurements are taken at different temporal
frequencies, separation is likely to improve; however, additional complexities in modelling time
variability concurrently with this high spectral dimensionality may be created. As a result, the applicability of ensemble-based techniques suitable for high-dimensional data is examined in this research,
together with the statistical evaluation of time-induced variability, since spectral measurements of tree
species were taken at different time periods. Classification errors for the stochastic gradient boosting
(SGB) and random forest (RF) methods ranged between 5.6% and 13.5%, respectively. Differences in
classification accuracy or errors were also accounted for in the assessment of the models, with up
to 46% of variation in classification error due to the effect of time in the RF model, indicating that
measurement time is important in improving discrimination between tree species. This is because
optical leaf characteristics can vary during the course of the year due to seasonal effects, health status,
or the developmental stage of a tree. Different spectral properties (assumed from relevant wavelength
bands) were found to be key factors impacting the models’ discrimination performance at various
measurement times.
