Blind hyperspectral sparse unmixing based on online dictionary learning

Including the estimation of endmembers and fractional abundances in hyperspectral images (HSI), blind hyperspectral unmixing (HU) is one of the most prominent research topics in image and signal processing for hyperspectral remote sensing. In this paper, a method of blind HU based on online dictionary learning and sparse coding is proposed, for the condition of the spectral signatures unknown in the HSI. An online optimization algorithm based on stochastic approximations is used for dictionary learning, which performs the optimization on the sparse coding and dictionary atoms alternately. On the sparse coding, a fully constrained least squares (FCLS) problem is solved because of the physical significance of fractional abundances. To estimate the endmembers in the HSI, a kind of clustering algorithm is used to cluster the atoms in the pruned dictionary obtained via the statistics on the sparse codes. With the estimated endmembers, the final fractional abundances can be obtained by using a variable splitting augmented Lagrangian and total variation algorithm. The experimental results with the synthetic data and the real-world data illustrate the effectiveness of the proposed approach.