Smoke segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations due to insufficient spectral information, particularly struggling with cloud interference and semi-transparent smoke regions. To address these challenges, we introduce hyperspectral imaging for smoke segmentation and present the first hyperspectral smoke segmentation dataset (HSSDataset) with carefully annotated samples collected from over 18,000 frames across 20 real-world scenarios using a Many-to-One annotations protocol. However, different spectral bands exhibit varying discriminative capabilities across spatial regions, necessitating adaptive band weighting strategies. We decompose this into three technical challenges: spectral interaction contamination, limited spectral pattern modeling, and complex weighting router problems. We propose a mixture of prototypes (MoP) network with: (1) Band split for spectral isolation, (2) Prototype-based spectral representation for diverse patterns, and (3) Dual-level router for adaptive spatial-aware band weighting. We further construct a multispectral dataset (MSSDataset) with RGB-infrared images. Extensive experiments validate superior performance across both hyperspectral and multispectral modalities, establishing a new paradigm for spectral-based smoke segmentation.

翻译：烟雾分割对于野火管理和工业安全应用至关重要。传统的基于可见光的方法由于光谱信息不足而面临局限，尤其在处理云层干扰和半透明烟雾区域时存在困难。为应对这些挑战，我们引入高光谱成像技术进行烟雾分割，并提出了首个高光谱烟雾分割数据集（HSSDataset）。该数据集采用多对一标注协议，从20个真实场景中采集超过18,000帧图像，并进行了精细标注。然而，不同光谱波段在不同空间区域表现出差异化的判别能力，这需要自适应波段加权策略。我们将此问题分解为三个技术挑战：光谱交互污染、有限的光谱模式建模以及复杂的加权路由问题。为此，我们提出了一种原型混合网络，其包含：（1）用于光谱隔离的波段分割，（2）基于原型的多样化光谱表征，以及（3）用于自适应空间感知波段加权的双级路由器。我们还构建了一个包含RGB-红外图像的多光谱数据集。大量实验验证了该方法在高光谱和多光谱模态上的优越性能，为基于光谱的烟雾分割建立了新范式。