Sparse Unmixing Algorithm for Hyperspectral Imagery

As a result of the spatial consideration of the imagery, spatial sparse unmixing (SU) can improve the unmixing accuracy for hyperspectral imagery, based on the application of a spectral library and sparse representation. To better utilize the spatial information, spatial SU methods such as SU via variable splitting augmented Lagrangian and total variation (SUnSAL-TV) and nonlocal SU (NLSU) have been proposed. However, the spatial information considered in these algorithms comes from the estimated abundance maps, which will change along with the iterations. As the spatial correlations of the imagery are fixed and certain, the spatial relationships obtained from the variable abundances are not reliable during the process of optimization.

To obtain more precise and fixed spatial relationships, an improved weight calculation NLSU (I-NLSU) algorithm is proposed in this letter by changing the spatial information acquisition source from the variable estimated abundances to the original hyperspectral imagery. A noise-adjusted principal component analysis strategy is also applied for the feature extraction in the proposed algorithm, and the obtained principal components are the foundation of the spatial relationships. The experimental results of both simulated and real hyperspectral data sets indicate that the proposed I-NLSU algorithm outperforms the previous spatial SU methods.