Non-dictionary Aided Sparse Unmixing of Hyperspectral Images via Weighted Nonnegative Matrix Factorization

In this paper, we propose a method of blind (non-dictionary aided) sparse hyperspectral unmixing for the linear mixing model (LMM). In this method, both the spectral signatures of materials (end members) (SSoM) and their fractional abundances (FAs) are supposed to be unknown and the goal is to find the matrices represent SSoM and FAs. The proposed method employs a weighted version of the non-negative matrix factorization (WNMF) in order to mitigate the impact of pixels that suffer from a certain level of noise (i.e., low signal-to-noise-ratio (SNR) values). We formulate the WNMF problem thorough the regularized sparsity terms of FAs and use the multiplicative updating rules to solve the acquired optimization problem. The effectiveness of proposed method is shown through the simulations over real hyperspectral data set and compared with several competitive unmixing methods.