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

TL;DR

This paper introduces a hyperspectral band selection method combining information theory and image quality metrics to reduce metamerism in urban driving perception systems, improving VRU detection.

Contribution

It proposes a novel spectral band selection strategy using CSNR and JMIM techniques to enhance material discrimination in hyperspectral images for automotive safety.

Findings

Selected bands significantly increase dissimilarity metrics between VRU and background.

The method outperforms RGB in perceptual and dissimilarity measures, reducing metameric confusion.

Improved spectral input enhances VRU separability for autonomous driving applications.

Abstract

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 $\Delta E$) 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.