Prediction Accuracy of Color Imagery from Hyperspectral Imagery

In this paper we present the utilization of high-spectral resolution imagery for improving low-spectral resolution imagery. In our analysis, we assume that an acquisition of high spectral resolution images provides more accurate spectral predictions of low spectral resolution images than a direct acquisition of low spectral resolution images. We illustrate the advantages by focusing on a specific case of images acquired by a hyperspectral (HS) camera and a color (red, green, and blue or RGB) camera. First, we identify two directions for utilization of HS images, such as (a) evaluation and calibration of RGB colors acquired from commercial color cameras, and (b) color quality improvement by achieving sub-spectral resolution. Second, we elaborate on challenges of RGB color calibration using HS information due to non-ideal illumination sources and non-ideal hyperspectral camera characteristics. We describe several adjustment (calibration) approaches to compensate for wavelength and spatial dependencies of real acquisition systems. Finally, we evaluate two color cameras by establishing ground truth RGB values from hyperspectral imagery and by defining pixel-based, correlation-based and histogram-based error metrics. Our experiments are conducted with three illumination sources (fluorescent light, Oriel Xenon lamp and incandescent light); with one HS Opto-Knowledge Systems camera and two color (RGB) cameras, such as Sony and Canon. We show a data-driven color-calibration as a method for improving image color quality. The applications of the developed techniques for HS to RGB image calibrations and sub-spectral resolution predictions are related to real-time model-based scene classification and scene simulation.