Spatial-spectral graph regularized sparse nonnegative matrix factorization hyperspectral unmixing

Abstract

Compared with traditional remote sensing images, hyperspectral images have the advantages of high spectral resolution, combining images with spectrum, and continuous spectrum. The phenomenon of mixed pixels in hyperspectral images seriously affects the accuracy of distinguishing objects on the ground, and has always been an important problem that hinders the further development of this technology. The most effective way to solve the mixed pixel problem is to perform mixed pixel unmixing. The purpose of hyperspectral unmixing is to obtain pure spectrum (endmembers) and their corresponding proportions (abundance). The nonnegative matrix factorization (NMF) technique has been widely adopted in the hyperspectral images unmixing problem due to its own advantages. The NMF method based on sparsity constraint can achieve better unmixing effect because of fully using of the sparse characteristic of the data. However, the unmixing model based on the sparse NMF still has shortcomings. Hyperspectral images contain a large amount of geometric structural information, which is not considered by most existing sparse NMF methods. To address those shortcomings, new regularization terms and weights can be introduced into the NMF model to better promote the unmixing performance. To solve this problem, a novel unmixing algorithm named spatial-spectral graph regularized sparse non-negative matrix factorization (SSGNMF) algorithm is proposed in this paper. Most of the sparse constrained unmixing algorithms have the problem of insufficient prior representation of abundance sparsity and using of spatial information insufficiently. On the one hand, the model of SSGNMF introduces graph regularization to preserve high-dimensional spatial information in hyperspectral images. On the other hand, the spatial weighting factor enables more spatial information to be incorporated into the unmixing model, and the spectral weighting factor can promote row sparsity of abundance matrices. By comparing with other classical algorithms, simulated and real hyperspectral data experimental results demonstrate that the introduction of dual weights and graph regularization can improve the unmixing effect, which verifies the validity of this algorithm. In addition, the experimental results also show that the graph regularization term and dual weights introduced in the NMF model in this paper can indeed promote the hyperspectral image unmixing performance well.