Multiple-Priors Ensemble Constrained Nonnegative Matrix Factorization for Spectral Unmixing

Abstract

Nonnegative matrix factorization (NMF) is widely used in unmixing issue in recent years, because it can simultaneously estimate the endmembers and abundances. However, most existing NMF-based methods only consider single matrix constraints and the other one is ignored. In fact, due to the influence of various noise, the regularization effectiveness based on the single matrix constraint method may be limited. In addition, hyperspectral images contain a variety of prior information, while many approaches usually only consider one of the priors , and the synergistic effect of multiple priors unions and two matrix joint constraints is neglected. In this article, to overcome this limitation, we propose a new blind unmixing scheme, called multiple- priors ensemble constrained NMF. The article first analyses the HSI intrinsic feature priors from both geometric and statistical aspects, and three important priors learners are defined. Then, three learners are jointly introduced into the NMF model and work together for the first time to impose constraints on both the endmember and the abundance matrix. In order to effectively solve the proposed model, Barzilai–Borwein stepsize strategy accelerates optimization algorithm is developed by using the variable splitting and augmented Lagrangian framework. The effectiveness and superiority of the proposed method are demonstrated by comparing with other advanced approaches on both synthetic and real world datasets.