Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery

Recently, many blind source separation (BSS)-based techniques have been applied to hyperspectral unmixing. In this paper, a new blind spectral unmixing method based on sparse component analysis (BSUSCA) is proposed to solve the problem of highly mixed data. The BSUSCA algorithm consists of an alternative scheme based on two-block alternating optimization, by which we can simultaneously obtain the endmember signatures and their corresponding fractional abundances. According to the spatial distribution of the endmembers, the sparse properties of the fractional abundances are considered in the proposed algorithm. A sparse component analysis (SCA)-based mixing matrix estimation method is applied to update the endmember signatures, and the abundance estimation problem is solved by the alternating direction method of multipliers (ADMM). SCA is utilized for the unmixing due to its various advantages, including the unique solution and robust modeling assumption. The robustness of the proposed algorithm is verified through simulated experimental study. The experimental results using both simulated data and real hyperspectral remote sensing images confirm the high efficiency and precision of the proposed algorithm.