Nonlinear Hyperspectral Unmixing Via Modelling Band Dependent Nonlinearity

Wavelength dependent nonlinearity is an essential issue in hyperspectral unmixing, which was overlooked in the past. In this paper, a band-wise nonlinear unmixing method is presented. An extended multilinear mixing model is adopted for interpreting different degrees of nonlinear contributions per band. Moreover, regularizers including abundances' sparsity and nonlinear parameters' smoothness are exploited to formulate the optimization problem and obtain better unmixing results. Finally, unmixing is implemented in the scheme of alternating direction method of multipliers. Experimental results on both simulated and real hyperspectral data validate that the proposed method can improve the unmixing accuracy and reveal the change of nonlinearity at each band as well.