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The key technology to overcome the drawbacks of hyperspectral imaging(expensive, high capture delay, and low spatial resolution) and make it widelyapplicable is to select only a few representative bands from hundreds of bands.However, current band selection (BS) methods face challenges in faircomparisons due to inconsistent train/validation settings, including the numberof bands, dataset splits, and retraining settings. To make BS methods easy andreproducible, this paper presents the first band selection search benchmark(BSS-Bench) containing 52k training and evaluation records of numerous bandcombinations (BC) with different backbones for various hyperspectral analysistasks. The creation of BSS-Bench required a significant computational effort of1.26k GPU days. By querying BSS-Bench, BS experiments can be performed easilyand reproducibly, and the gap between the searched result and the bestachievable performance can be measured. Based on BSS-Bench, we further discussthe impact of various factors on BS, such as the number of bands, unsupervisedstatistics, and different backbones. In addition to BSS-Bench, we present aneffective one-shot BS method called Single Combination One Shot (SCOS), whichlearns the priority of any BCs through one-time training, eliminating the needfor repetitive retraining on different BCs. Furthermore, the search process ofSCOS is flexible and does not require training, making it efficient andeffective. Our extensive evaluations demonstrate that SCOS outperforms currentBS methods on multiple tasks, even with much fewer bands. Our BSS-Bench andcodes are available in the supplementary material and will be publiclyavailable.

BSS-Bench: Towards Reproducible and Effective Band Selection Search