Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging

Estimating reflectance is key when working with hyperspectral cameras. The modelling of light sources can aid reflectance estimation, however, it is commonly overlooked. The key contribution of this letter is a physics-based, data-driven model formed by a Gaussian Process (GP) with a unique mean function capable of modelling a light source with an asymmetric radiant intensity distribution (RID) and a configurable attenuation function. This is referred to as the light-source-mean model. Moreover, we argue that by utilising multi-modal sensing information, we can achieve improved reflectance estimation using the proposed light source model with shape information obtained by depth cameras. An existing reflectance estimation method, that solves the dichromatic reflectance model (DRM) via quadratic programming optimisation, is augmented with terms that allow input of shape information. Experiments in simulation show that the light-source-mean GP model had less error when compared to a parametric model. The improved reflectance estimation outperforms existing methods in simulation by reducing the error by 96.8% on average when compared to existing works. We further validate the improved reflectance estimation method through a multi-modal classification application.

Automated summary

This study proposes a physics-based, data-driven light-source-mean model for reflectance estimation, achieving improved accuracy by utilizing multi-modal sensing information and shape information obtained by depth cameras. Experimental results demonstrate that the proposed model outperforms existing methods, reducing the error by 96.8% on average. The improved reflectance estimation method is further validated through a multi-modal classification application.