用高光谱成像提升城市驾驶中行人分割精度，显著减少误判。
Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study
- 通过优化波段选择提升高光谱数据表现，比RGB更准确区分行人与背景。
- 在行人分割上平均提升1.44% IoU和2.18% F1-score，骑行者分割也明显改善。
- 适合自动驾驶安全系统研发者，尤其关注感知鲁棒性的场景。
自动驾驶感知系统中的行人分割因RGB成像的色觉欺骗（metamerism）面临严重安全挑战，导致行人与背景视觉上难以区分。本研究基于Hyperspectral City v2（H-City）数据集，探讨高光谱成像（HSI）在城市驾驶场景下增强行人分割的潜力。将128通道的HSI数据通过主成分分析（PCA）与基于对比信噪比联合互信息最大化（CSNR-JMIM）的最优波段选择，转换为三通道表示，并对比了标准RGB。评估了U-Net、DeepLabV3+和SegFormer三种语义分割模型。结果表明，采用CSNR-JMIM方法时，行人分割的平均交并比（IoU）提升1.44%，F1分数提升2.18%；骑行者分割同样获得1.43% IoU和2.25% F1分数提升。性能提升源于优化波段选择带来的更强光谱区分能力，有效降低误检率。该研究证明，通过最优波段选择实现的高光谱成像可显著提升自动驾驶中的行人分割可靠性，具有重要安全应用价值。
原文摘要 · Abstract (English)
Pedestrian segmentation in automotive perception systems faces critical safety challenges due to metamerism in RGB imaging, where pedestrians and backgrounds appear visually indistinguishable.. This study investigates the potential of hyperspectral imaging (HSI) for enhanced pedestrian segmentation in urban driving scenarios using the Hyperspectral City v2 (H-City) dataset. We compared standard RGB against two dimensionality-reduction approaches by converting 128-channel HSI data into three-channel representations: Principal Component Analysis (PCA) and optimal band selection using Contrast Signal-to-Noise Ratio with Joint Mutual Information Maximization (CSNR-JMIM). Three semantic segmentation models were evaluated: U-Net, DeepLabV3+, and SegFormer. CSNR-JMIM consistently outperformed RGB with an average improvements of 1.44% in Intersection over Union (IoU) and 2.18% in F1-score for pedestrian segmentation. Rider segmentation showed similar gains with 1.43% IoU and 2.25% F1-score improvements. These improved performance results from enhanced spectral discrimination of optimally selected HSI bands effectively reducing false positives. This study demonstrates robust pedestrian segmentation through optimal HSI band selection, showing significant potential for safety-critical automotive applications.
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