文献类型：期刊文献

英文题名：Hyperspectral Smoke Segmentation via Mixture of Prototypes

作者：Yao, Lujian[1]; Zhao, Haitao[1]; Kong, Xianghai[1]; Xu, Yuhan[1]

机构：[1] Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Meilong Rd, No.130, Shanghai, 200237, China

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260114349)

语种：英文

外文关键词：Accident prevention - Artificial intelligence - Hyperspectral imaging - Image segmentation - Infrared imaging - Routers - Smoke

摘要：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. Copyright ? 2026, The Authors. All rights reserved.