用高光谱波段选优提升城市驾驶中弱势路权者识别，减少视觉混淆。
CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving
- 融合互信息与信噪比指标，选出3个最具区分力的光谱波段。
- 在多个度量上提升70%至1200%以上，显著降低材料混淆。
- 适合自动驾驶感知系统研发者，尤其关注夜间或低光照场景。
保护弱势道路使用者（VRU）是汽车感知系统面临的关键安全挑战，尤其是在受色觉欺骗（异色同形）影响的视觉模糊情况下，不同材质在RGB图像中呈现相似外观。本文利用高光谱成像（HSI）技术，通过捕捉可见光以外的近红外（NIR）独特材料特征来克服此限制。为应对HSI数据固有的高维度问题，提出一种结合信息论方法（联合互信息最大化、相关性分析）与新型图像质量指标（对比信噪比，CSNR）的波段选择策略，以识别最具光谱信息的波段。基于Hyperspectral City V2（H-City）数据集，筛选出三个关键波段（497 nm、607 nm、895 nm，±27 nm），并重建伪彩色图像与共配准的RGB图像进行对比。定量结果显示，对VRU与背景的差异性和可感知分离性均有显著提升：在欧氏距离、光谱角距离（SAM）、T²检验及感知差异（CIE ΔE）四项指标上，分别提升70.24%、528.46%、1206.83%和246.62%，全面优于RGB，证实了异色同形混淆的明显降低。该方法提供经光谱优化的输入，增强下游感知任务中对VRU的可分性，为高级驾驶辅助系统（ADAS）与自动驾驶（AD）奠定坚实基础，助力提升道路安全。
原文摘要 · Abstract (English)
Protecting Vulnerable Road Users (VRU) is a critical safety challenge for automotive perception systems, particularly under visual ambiguity caused by metamerism, a phenomenon where distinct materials appear similar in RGB imagery. This work investigates hyperspectral imaging (HSI) to overcome this limitation by capturing unique material signatures beyond the visible spectrum, especially in the Near-Infrared (NIR). To manage the inherent high-dimensionality of HSI data, we propose a band selection strategy that integrates information theory techniques (joint mutual information maximization, correlation analysis) with a novel application of an image quality metric (contrast signal-to-noise ratio) to identify the most spectrally informative bands. Using the Hyperspectral City V2 (H-City) dataset, we identify three informative bands (497 nm, 607 nm, and 895 nm, $\pm$27 nm) and reconstruct pseudo-color images for comparison with co-registered RGB. Quantitative results demonstrate increased dissimilarity and perceptual separability of VRU from the background. The selected HSI bands yield improvements of 70.24%, 528.46%, 1206.83%, and 246.62% for dissimilarity (Euclidean, SAM, $T^2$) and perception (CIE $ΔE$) metrics, consistently outperforming RGB and confirming a marked reduction in metameric confusion. By providing a spectrally optimized input, our method enhances VRU separability, establishing a robust foundation for downstream perception tasks in Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD), ultimately contributing to improved road safety.
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