Hyperspectral Shadow Removal via Nonlinear Unmixing

Removing shadows that are often present in remotely sensed hyperspectral images is important for both enhancing the interpretability of the data and further target analysis. Shadow removal approaches based on spectral unmixing have been proposed in the literature using the linear mixture model. However, objects that produce shadows may also introduce light scattering, and the higher order interactions of photons can cause nonlinearity. This letter integrates the nonlinear hyperperspectral unmixing into the unmixing-based shadow removal, and the effects of applying typical nonlinear algorithms within the approach are investigated. The usefulness of nonlinear unmixing in hyperspectral shadow removal is verified based on the results of applications to both laboratory-created real data and actual airborne data.