Double Weighted Sparse Nonnegative Tensor Factorization for Hyperspectral Unmixing

A variety of unmixing methods offered fruitful solutions for extracting endmembers and estimating abundances. Recently, a matrix-vector nonnegative tensor factorization (MV-NTF) unmixing method was proposed. Compared with nonnegative matrix factorization (NMF), NTF avoids the conversion of hyperspectral data from 3-D to 2-D, thereby preserving the intrinsic structure information. Nevertheless, MV-NTF ignores local spatial information owing to dealing with data as a whole. Thus, in this letter, to make the most of spatial information and abundance sparsity, a new double weighted sparse NTF (DWSNTF) unmixing method is proposed. Under the MV-NTF framework, a double weighted L-1 regularizer is firstly utilized to characterize more precise and sparse abundance maps. One weight acts on single pixel to promote the sparsity of solution, while the other weight exploits the local spatial information to conserve more details and prevent oversmoothness. In addition, all weights are stacked into a weight tensor to fit the higher-dimensional factorization and facilitate optimization. Experimental results on both synthetic and real data demonstrate the validity and superiority of our proposed method against the state-of-the-art methods.

Automated summary

This letter proposes a new double weighted sparse NTF (DWSNTF) unmixing method to make the most of spatial information and abundance sparsity. By utilizing a double weighted L-1 regularizer, more precise and sparse abundance maps can be characterized, while preserving more details and preventing oversmoothness. The experimental results demonstrate the validity and superiority of the proposed method against the state-of-the-art methods on both synthetic and real data.