CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving

Protecting Vulnerable Road Users (VRU) remains a key challenge in automotive perception, particularly when RGB imagery exhibits metameric ambiguity under challenging illumination. Hyperspectral Imaging (HSI) can provide material-dependent cues beyond RGB, including in the Near-Infrared (NIR), but its high dimensionality limits deployment in automotive systems. This paper proposes a compact band-selection pipeline that combines joint mutual information maximization with a patch-based contrast signal-to-noise ratio criterion to select a practical 3-band HSI subset for VRU perception. On the Hyperspectral City V2 dataset, the proposed pipeline selects a VIS-NIR triplet at 521 nm, 753 nm, and 903 nm. We evaluate VRU-Road separability under higher-illumination scenes and an objectively defined low-illumination subset using per-scene distributions, and bootstrap confidence intervals. Results are reported in both a metric-ready representation (for direct comparison with co-registered RGB) and a sensor-native representation. Downstream utility is further evaluated by semantic segmentation using U-Net, DeepLabV3+, and PSPNet over three random seeds. The selected triplet improves VRU-Road separability on key criteria, remains robust under low illumination, and yields competitive downstream performance among the evaluated HSI-derived inputs. Overall, the proposed approach reduces spectral dimensionality from 128 bands to 3 (97.7% reduction) and supports compact multispectral sensing as a practical complement to RGB for robust VRU perception.