Non-local euclidean medians sparse unmixing for hyperspectral remote sensing imagery

Sparse unmixing based on sparse representation theory has been successfully applied to hyperspectral remote sensing imagery. To better utilize the abundant spatial information and improve the unmixing accuracy, spatial sparse unmixing methods such as non-local sparse unmixing (NLSU) have been proposed. Although the NLSU method utilizes the nonlocal spatial information as its spatial regularization term, and obtains a satisfactory unmixing accuracy, the final abundances are affected by the non-local neighborhoods and drift away from the true abundance values when the hyperspectral images are contaminated by strong noise. To solve this problem, a non-local Euclidean medians sparse unmixing (NLEMSU) method is proposed to improve NLSU by replacing the non-local means total variation spatial consideration with non-local Euclidean medians filtering approach. The experimental results using simulated and real hyperspectral images indicate that NLEMSU outperforms the previous sparse unmixing algorithms and, hence, provides an effective option for the unmixing of hyperspectral remote sensing imagery.