  Hyperspectral Imaging (HSI) plays an increasingly critical role in precise
vision tasks within remote sensing, capturing a wide spectrum of visual data.
Transformer architectures have significantly enhanced HSI task performance,
while advancements in Transformer Architecture Search (TAS) have improved model
discovery. To harness these advancements for HSI classification, we make the
following contributions: i) We propose HyTAS, the first benchmark on
transformer architecture search for Hyperspectral imaging, ii) We
comprehensively evaluate 12 different methods to identify the optimal
transformer over 5 different datasets, iii) We perform an extensive factor
analysis on the Hyperspectral transformer search performance, greatly
motivating future research in this direction. All benchmark materials are
available at HyTAS.
