Shadow modelling and correction techniques in hyperspectral imaging

Abstract

Shadowing and illumination variations, caused by scene topography or non-uniform lighting, are present in virtually all image data. Given its prevalence and nuisance value when making quantitative image measurements, the development of accurate shading compensation techniques is essential. The linear mixture model of image formation is broadly employed when dealing with hyperspectral data but makes no explicit provision for shading and this can lead to inaccurate analysis results. In this reported work, the effect of shading on a hyperspectral image is demonstrated, and new methods for mitigating the effect using a forced-zero endmember and conical sub-simplex projection are presented. It is shown that together they provide the most accurate and theoretically sound shadow-corrected data.