Endmember extraction algorithms (EEAs) are among the most commonly discussed types of hyperspectral image processing in the past three decades. This article proposes a spatial energy prior constrained maximum simplex volume (SENMAV) approach for spatial-spectral endmember extraction of hyperspectral images. SENMAV investigates the spatial information from the perspective of the spatial energy prior of a Markov random field (MRF), which is used as a regularization term of the traditional maximum volume simplex model to simultaneously constrain the selection of the endmembers in both the spatial and spectral viewpoints. This article sheds new light on spatial-spectral-based EEAs, as SEN-MAV well balances the tradeoff between endmember extraction accuracy and spatial attribute requirements of endmembers. Based on the spectral angle distance and root-mean-square error, experimental results on both synthetic and real hyperspectral datasets indicate that the proposed approach significantly improves the endmember extraction performance over current state-of-the-art spatial-spectral-based EEAs.

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.

The problem of multispectral and hyperspectral image fusion (MHF) is to reconstruct images by fusing the spatial information of multispectral images and the spectral information of hyperspectral images. Focusing on the problem that the hyperspectral canonical polyadic decomposition model and the Tucker model cannot introduce the physical interpretation of the latent factors into the framework, it is difficult to use the known properties and abundance of endmembers to generate high-quality fusion images. This paper proposes a new fusion algorithm. In this paper, a coupled non-negative block-term tensor model is used to estimate the ideal high spatial resolution hyperspectral images, its sparsity is characterized by adding 1-norm, and total variation (TV) is introduced to describe piecewise smoothness. Secondly, the different operators in two directions are defined and introduced to characterize their piecewise smoothness. Finally, the proximal alternating optimization (PAO) algorithm and the alternating multiplier method (ADMM) are used to iteratively solve the model. Experiments on two standard datasets and two local datasets show that the performance of this method is better than the state-of-the-art methods.

No abstract