Hyperspectral Unmixing Using Reweighted Unidirectional TV Low-Rank NTF With Multiple-Factor Collaboration Regularization

The purpose of hyperspectral unmixing (HU) is to extract the spectral signatures and their proportion fractions from the hyperspectral remote sensing image (HSIs), which is a crucial issue in HSIs processing. Recently, nonnegative tensor factorization (NTF) has been successfully applied in the field of HU, because a third-order tensor can effectively maintain the spatial–spectral structure of HSIs, and NTF can produce multiple-view factor matrices that can impose more natural and diverse constraints. However, current NTF unmixing models utilizing priors are still insufficient, and NTF does not fully utilize the multiview factor matrices. Moreover, most methods only consider single-factor constraints, neglecting the effectiveness of collaborative constraints among multiple-factor priors information. To address the aforementioned shortcomings, a reweighted unidirectional total variation (UTV) low-rank NTF with multiple-factor collaborative regularization is proposed in this article. Specifically, first, to further exploit the abundance prior information, by utilizing the spatial similarity and the group sparse structure between the pixels of the two abundance low-rank submatrices, two reweighted UTV regularizers are constructed, which enhance the smooth and sparse properties of the abundances. Second, to smooth the endmember spectra and reduce the irregular burrs phenomenon caused by spectral noise interference, we introduce endmember band difference smooth constraints to ensure the piecewise smoothness of the endmember spectrum. Then, two reweighted UTV regularizers and an endmember spectral smooth regularizer are jointly introduced into the low-rank NTF framework to collaboratively regularize the nonconvex tensor representation model. Finally, we adopt the alternating direction multiplier method to optimize the objective model, and a large number of experiments on both synthetic and real hyperspectral datasets validate the effectiveness and efficiency of the proposed approach.