  Hyperspectral Imaging (HSI) serves as a non-destructive spatial spectroscopy
technique with a multitude of potential applications. However, a recurring
challenge lies in the limited size of the target datasets, impeding exhaustive
architecture search. Consequently, when venturing into novel applications,
reliance on established methodologies becomes commonplace, in the hope that
they exhibit favorable generalization characteristics. Regrettably, this
optimism is often unfounded due to the fine-tuned nature of models tailored to
specific HSI contexts.
  To address this predicament, this study introduces an innovative benchmark
dataset encompassing three markedly distinct HSI applications: food inspection,
remote sensing, and recycling. This comprehensive dataset affords a finer
assessment of hyperspectral model capabilities. Moreover, this benchmark
facilitates an incisive examination of prevailing state-of-the-art techniques,
consequently fostering the evolution of superior methodologies.
  Furthermore, the enhanced diversity inherent in the benchmark dataset
underpins the establishment of a pretraining pipeline for HSI. This pretraining
regimen serves to enhance the stability of training processes for larger
models. Additionally, a procedural framework is delineated, offering insights
into the handling of applications afflicted by limited target dataset sizes.

Computer Vision and Pattern Recognition