Optical leaf parameter estimation based on directional characteristics of leaf-scale hyperspectral images

With the advent of UAVs and lightweight hyperspectral imagers, leaf-scale hyperspectral images of agricultural fields are available by low-altitude observation. Although leaf-scale hyperspectral images eliminate the effect of volume scattering and spectral mixing, the hyperspectral profile is affected by the surface shape of the leaf, i.e., shade and shadow. Because diffuse reflection of leaves has directional characteristics, the normalized vectors of the diffuse reflection are distributed around a point on a unit hypersphere. The center of the diffuse reflection is equivalent to the Lambert coefficients of the leaves. In this paper, we propose a method in which Lambert coefficients are estimated by investigating directional characteristics of leaf-scale hyperspectral images based on von Mises-Fisher distribution (vMF).