全变差稀疏约束深度非负矩阵分解高光谱遥感影像解混方法.

The traditional nonnegative matrix factorization methods are based on single-layer model. The deep nonnegative matrix factorization methods are based on the mathematical theory, ignoring the actual spectral mixing process of materials. This paper started from the physical process of spectral mixing, combining the advantages of traditional nonnegative matrix factorization models and deep learning. Aiming at the problem that the deep nonnegative matrix factorization methods ignore the actual spectral mixing process of materials, the spectral mixing process is reversed from the physical spectral mixing process. Furthermore, considering the sparsity and spatial smoothness of the distribution of the endmembers in the two-dimensional space, the total variation and sparsity regularized deep nonnegative matrix factorization based on the endmember matrix is proposed. This paper verified the effectiveness of the proposed method through synthetic experiments and real data experiments, which is compared with five classical hyperspectral unmixing methods. Compared with SDNMF-TV, SAD and RMSE are reduced. The results both in the synthetic experiments and the real experiments shows that the method in this paper achieves the best unmixing results. [ABSTRACT FROM AUTHOR]