Blind hyperspectral unmixing by non-parametric non-Gaussianity measure. (2018) - Wang, Fasong; Li, Rui
For linear mixing model (LMM) of hyperspectral unmixing in hyperspectral images processing problem, the endmember fractional abundances satisfy the sum-to-one constraint, which makes the well-known independent component analysis (ICA) based blind source separation (BSS) algorithms not well suited to blind hyperspectral unmixing (bHU). In this paper, an efficient non-parametric bHU algorithm consulting dependent component analysis (DCA) is presented. Based on the cumulative density function (CDF) and order statistics instead of traditional probability distribution function (PDF), the novel objective function is derived by maximising the non-parametric non-Gaussianity between the estimated endmember abundance of the endmember signatures and their corresponding original abundances. With the stochastic gradient rule of constrained optimisation method, an efficient dependent sources separation algorithm for bHU is obtained to fulfil the endmember signatures extraction and abundances estimation tasks. Simulations based on the synthetic data are performed to evaluate the validity of the proposed non-parametric non-Gaussianity HU (non-pNG-bHU) algorithm.