Supervised Kernel Based Nonlinear Unmixing of Hyperspectral Data

Abstract

In hyperspectral imagery problem, pixels are mixtures of spectral component associated with pure materials. Recently, nonlinear models have been taken into consideration to surmount some limitations of linear model. In this paper, the nonlinear hyperspectral image unmixing problem is formulated with kernel learning theory, with the number of kernels being controlled by the coherence rule. To be more physically interpretable, a relationship between endmembers and abundance vectors is introduced as a constraint of the optimization problem. An iterative learning algorithm derived from augmented Lagrangian method is proposed to solve the defined problem. Simulation results show the efficacy of the proposed model and algorithm.