  Hyperspectral imaging sensors are becoming increasingly popular in robotics
applications such as agriculture and mining, and allow per-pixel thematic
classification of materials in a scene based on their unique spectral
signatures. Recently, convolutional neural networks have shown remarkable
performance for classification tasks, but require substantial amounts of
labelled training data. This data must sufficiently cover the variability
expected to be encountered in the environment. For hyperspectral data, one of
the main variations encountered outdoors is due to incident illumination, which
can change in spectral shape and intensity depending on the scene geometry. For
example, regions occluded from the sun have a lower intensity and their
incident irradiance skewed towards shorter wavelengths.
  In this work, a data augmentation strategy based on relighting is used during
training of a hyperspectral convolutional neural network. It allows training to
occur in the outdoor environment given only a small labelled region, which does
not need to sufficiently represent the geometric variability of the entire
scene. This is important for applications where obtaining large amounts of
training data is labourious, hazardous or difficult, such as labelling pixels
within shadows. Radiometric normalisation approaches for pre-processing the
hyperspectral data are analysed and it is shown that methods based on the raw
pixel data are sufficient to be used as input for the classifier. This removes
the need for external hardware such as calibration boards, which can restrict
the application of hyperspectral sensors in robotics applications. Experiments
to evaluate the classification system are carried out on two datasets captured
from a field-based platform.
