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.

翻译：摘要：克服高光谱成像缺陷（成本高昂、捕获延迟高、空间分辨率低）并使其广泛应用的关键技术，是从数百个波段中仅选取少量代表性波段。然而，当前波段选择方法因训练/验证设置（包括波段数量、数据集划分及重训练设置）不一致，面临公平比较的挑战。为使波段选择方法简便且可复现，本文提出了首个波段选择搜索基准（BSS-Bench），其中包含52k条针对不同高光谱分析任务中多种波段组合（BC）与不同骨干网络的训练与评估记录。构建BSS-Bench需耗费约1.26k GPU·天的计算资源。通过查询BSS-Bench，可轻松且可复现地进行波段选择实验，并衡量搜索结果与最优性能之间的差距。基于BSS-Bench，我们进一步探讨了多种因素（如波段数量、无监督统计量及不同骨干网络）对波段选择的影响。除BSS-Bench外，我们还提出了一种高效的一次性波段选择方法——单一组合一次性搜索（SCOS），该方法通过一次性学习任意波段组合的优先级，避免了对不同波段组合的重复训练。此外，SCOS的搜索过程灵活且无需训练，兼具高效性与有效性。大量评估表明，SCOS在多项任务中均优于现有波段选择方法，即使使用更少的波段也能取得更优性能。我们的BSS-Bench及代码均可在补充材料中获取，并将公开提供。