  The key technology to overcome the drawbacks of hyperspectral imaging
(expensive, high capture delay, and low spatial resolution) and make it widely
applicable is to select only a few representative bands from hundreds of bands.
However, current band selection (BS) methods face challenges in fair
comparisons due to inconsistent train/validation settings, including the number
of bands, dataset splits, and retraining settings. To make BS methods easy and
reproducible, this paper presents the first band selection search benchmark
(BSS-Bench) containing 52k training and evaluation records of numerous band
combinations (BC) with different backbones for various hyperspectral analysis
tasks. The creation of BSS-Bench required a significant computational effort of
1.26k GPU days. By querying BSS-Bench, BS experiments can be performed easily
and reproducibly, and the gap between the searched result and the best
achievable performance can be measured. Based on BSS-Bench, we further discuss
the impact of various factors on BS, such as the number of bands, unsupervised
statistics, and different backbones. In addition to BSS-Bench, we present an
effective one-shot BS method called Single Combination One Shot (SCOS), which
learns the priority of any BCs through one-time training, eliminating the need
for repetitive retraining on different BCs. Furthermore, the search process of
SCOS is flexible and does not require training, making it efficient and
effective. Our extensive evaluations demonstrate that SCOS outperforms current
BS methods on multiple tasks, even with much fewer bands. Our BSS-Bench and
codes are available in the supplementary material and will be publicly
available.
